huangwb8/ChineseResearchLaTeX

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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rm -f main.aux main.log main.out main.bbl main.blg

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),

and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

#!/usr/bin/env python3

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

#!/usr/bin/env python3

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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if not d or (not _DOI_FORMAT_RE.match(d)):         return False     url = f"https://api.crossref.org/works/{quote(d)}"

return False     url = f"https://api.crossref.org/works/{quote(d)}"     try:

url = f"https://api.crossref.org/works/{quote(d)}"

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本文档记录 `nsfc-qc/` 的重要变更。格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

- **引用真伪/错引 + 逻辑闭环**这类“高歧义、要证据链”的 QC：优先用**更强推理/更高能力**的模型（必要时牺牲速度/成本）。OpenAI 的建议是：复杂、模糊、需要大量判断的任务更适合其 *reasoning* 系列模型。     参考：OpenAI《Reasoning best practices》。https://platform.openai.com/docs/guides/reasoning-best-practices

参考：OpenAI《Reasoning best practices》。https://platform.openai.com/docs/guides/reasoning-best-practices

参考：Anthropic《Tool use / Choosing a model》。https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/implement-tool-use

Anthropic 官方建议从**能力/速度/成本**三者权衡，并提供两种常见起步策略：   1) 先用更快更省的模型快速迭代，不够再升级；2) 复杂任务从最强模型起步，再逐步降档优化。   参考：Anthropic《Choosing the right model》。https://docs.anthropic.com/en/docs/about-claude/models/choosing-a-model

参考：Anthropic《Choosing the right model》。https://docs.anthropic.com/en/docs/about-claude/models/choosing-a-model

OpenAI 将模型区分为 *reasoning* 与非 *reasoning*（GPT）两类，并强调：*reasoning* 更适合复杂规划/决策/歧义信息处理；同时建议对 reasoning 模型“提示词更直接、更少花活”。   参考：OpenAI《Reasoning best practices》。https://platform.openai.com/docs/guides/reasoning-best-practices

参考：OpenAI《Reasoning best practices》。https://platform.openai.com/docs/guides/reasoning-best-practices

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def _http_get_json(url: str, *, timeout_s: int, user_agent: str) -> Optional[dict]:     try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "application/json"})

try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "application/json"})         with urllib.request.urlopen(req, timeout=timeout_s) as resp:

req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "application/json"})

with urllib.request.urlopen(req, timeout=timeout_s) as resp:

def _http_get_text(url: str, *, timeout_s: int, user_agent: str) -> Optional[str]:     try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "*/*"})

try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "*/*"})         with urllib.request.urlopen(req, timeout=timeout_s) as resp:

req = urllib.request.Request(url, headers={"User-Agent": user_agent, "Accept": "*/*"})

with urllib.request.urlopen(req, timeout=timeout_s) as resp:

try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent}, method="HEAD")

req = urllib.request.Request(url, headers={"User-Agent": user_agent}, method="HEAD")

with urllib.request.urlopen(req, timeout=timeout_s) as resp:

def _normalize_doi(raw: str) -> str:     d = (raw or "").strip()     d = d.replace("https://doi.org/", "").replace("http://doi.org/", "")

d = (raw or "").strip()     d = d.replace("https://doi.org/", "").replace("http://doi.org/", "")     d = d.replace("https://dx.doi.org/", "").replace("http://dx.doi.org/", "")

d = d.replace("https://doi.org/", "").replace("http://doi.org/", "")

d = d.replace("https://dx.doi.org/", "").replace("http://dx.doi.org/", "")

if not doi:         return {"ok": False, "error": "no_doi"}     url = "https://api.crossref.org/works/" + urllib.parse.quote(doi)

return {"ok": False, "error": "no_doi"}     url = "https://api.crossref.org/works/" + urllib.parse.quote(doi)     data = _http_get_json(url, timeout_s=timeout_s, user_agent=user_agent)

url = "https://api.crossref.org/works/" + urllib.parse.quote(doi)

if not arxiv_id:         return {"ok": False, "error": "no_arxiv_id"}     url = "http://export.arxiv.org/api/query?id_list=" + urllib.parse.quote(arxiv_id)

return {"ok": False, "error": "no_arxiv_id"}     url = "http://export.arxiv.org/api/query?id_list=" + urllib.parse.quote(arxiv_id)     txt = _http_get_text(url, timeout_s=timeout_s, user_agent=user_agent)

url = "http://export.arxiv.org/api/query?id_list=" + urllib.parse.quote(arxiv_id)

try:         root = ET.fromstring(txt)         ns = {"a": "http://www.w3.org/2005/Atom"}

root = ET.fromstring(txt)         ns = {"a": "http://www.w3.org/2005/Atom"}         entry = root.find("a:entry", ns)

ns = {"a": "http://www.w3.org/2005/Atom"}

pdf_url = link.attrib.get("href", "")         if not pdf_url:             pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf"

if not pdf_url:             pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf"         return {"ok": True, "source": "arxiv", "url": url, "title": title, "abstract": summary, "pdf_url": pdf_url}

pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf"

if not email:         return {"ok": False, "error": "missing_email"}     url = f"https://api.unpaywall.org/v2/{urllib.parse.quote(doi)}?email={urllib.parse.quote(email)}"

return {"ok": False, "error": "missing_email"}     url = f"https://api.unpaywall.org/v2/{urllib.parse.quote(doi)}?email={urllib.parse.quote(email)}"     data = _http_get_json(url, timeout_s=timeout_s, user_agent=user_agent)

url = f"https://api.unpaywall.org/v2/{urllib.parse.quote(doi)}?email={urllib.parse.quote(email)}"

dst.parent.mkdir(parents=True, exist_ok=True)     try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent})

try:         req = urllib.request.Request(url, headers={"User-Agent": user_agent})         with urllib.request.urlopen(req, timeout=timeout_s) as resp:

req = urllib.request.Request(url, headers={"User-Agent": user_agent})

with urllib.request.urlopen(req, timeout=timeout_s) as resp:

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),

and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

#!/usr/bin/env python3

#!/usr/bin/env python3

#!/usr/bin/env python3

def _http_get_json(url: str, timeout_s: int = 20) -> Tuple[Optional[dict], Optional[str]]:     req = urllib.request.Request(

req = urllib.request.Request(

)     try:         with urllib.request.urlopen(req, timeout=timeout_s) as resp:

try:         with urllib.request.urlopen(req, timeout=timeout_s) as resp:             payload = resp.read().decode("utf-8", errors="ignore")

with urllib.request.urlopen(req, timeout=timeout_s) as resp:

crossref_url = f"https://api.crossref.org/works/{doi_encoded_crossref}"

openalex_url = f"https://api.openalex.org/works/https://doi.org/{doi_encoded_openalex}"

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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本文档记录 `paper-explain-figures/` skill 的重要变更。格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

rm -rf .bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/paper-explain-figures

rm -rf .bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/paper-explain-figures

claude:     cmd: ["claude"]     global_args: ["--dangerously-skip-permissions", "--no-session-persistence"]

cmd: ["claude"]     global_args: ["--dangerously-skip-permissions", "--no-session-persistence"]     print_subcommand: ["-p"]

global_args: ["--dangerously-skip-permissions", "--no-session-persistence"]

#!/usr/bin/env python3

#!/usr/bin/env python3

"claude": {                 "cmd": ["claude"],                 "global_args": ["--dangerously-skip-permissions", "--no-session-persistence"],

"cmd": ["claude"],                 "global_args": ["--dangerously-skip-permissions", "--no-session-persistence"],                 "print_subcommand": ["-p"],

"global_args": ["--dangerously-skip-permissions", "--no-session-persistence"],

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)，版本号遵循 [语义化版本](https://semver.org/lang/zh-CN/)。

| 信息 | 来源 | 类型 | 访问日期 | 可信度 | |------|------|------|----------|--------| | 作者指南 | https://... | 官方 | YYYY-MM-DD | 高 |

|------|------|------|----------|--------| | 作者指南 | https://... | 官方 | YYYY-MM-DD | 高 | | 作者体验 | https://... | 社区 | YYYY-MM-DD | 中 |

| 作者指南 | https://... | 官方 | YYYY-MM-DD | 高 |

| 作者体验 | https://... | 社区 | YYYY-MM-DD | 中 |

#!/usr/bin/env python3

#!/usr/bin/env python3

timeout_seconds: 30   delay_seconds_between_requests: 0.34   esearch_url: "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"

delay_seconds_between_requests: 0.34   esearch_url: "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"   efetch_url: "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"

esearch_url: "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"

efetch_url: "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"

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#!/usr/bin/env python3

def http_get(url: str, timeout: int) -> str:     with urllib.request.urlopen(url, timeout=timeout) as response:

with urllib.request.urlopen(url, timeout=timeout) as response:

"journal": article.findtext(".//Journal/Title", default="").strip(),                 "publication_date": _extract_pub_date(article),                 "url": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else "",

"publication_date": _extract_pub_date(article),                 "url": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else "",             }

"url": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else "",

abstract=compact_text(article.get("abstract", ""), 140).replace("|", "/"),                     pmid=article["pmid"] or "N/A",                     url=article["url"] or "https://pubmed.ncbi.nlm.nih.gov/",

pmid=article["pmid"] or "N/A",                     url=article["url"] or "https://pubmed.ncbi.nlm.nih.gov/",                 )

url=article["url"] or "https://pubmed.ncbi.nlm.nih.gov/",

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"cas_small_category_quartile": "材料科学 2 区",       "recognition": "业内认可度高，主流实验室常投",       "official_website": "https://example.org/journal",

"recognition": "业内认可度高，主流实验室常投",       "official_website": "https://example.org/journal",       "scope_fit_summary": "论文主题与期刊 scope 匹配点。",

"official_website": "https://example.org/journal",

"relevance": "AI 说明这篇论文为何能支持该期刊进入最终推荐。",           "pmid": "12345678",           "url": "https://pubmed.ncbi.nlm.nih.gov/12345678/"

"pmid": "12345678",           "url": "https://pubmed.ncbi.nlm.nih.gov/12345678/"         }

"url": "https://pubmed.ncbi.nlm.nih.gov/12345678/"

"journal_name": "示例期刊",       "include_in_set2": true,       "official_website": "https://example.org/journal",

"include_in_set2": true,       "official_website": "https://example.org/journal",       "scope_fit_summary": "说明该刊 scope 为什么与论文匹配。",

"official_website": "https://example.org/journal",

{           "label": "官方 scope",           "url": "https://example.org/aims-and-scope"

"label": "官方 scope",           "url": "https://example.org/aims-and-scope"         }

"url": "https://example.org/aims-and-scope"

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),

and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

#!/usr/bin/env python3

本文档记录 `research-citation-check/` 的重要变更。历史记录中的 `check-review-alignment` 为旧名。格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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本文件遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

- [DeepPaperNote](https://github.com/917Dhj/DeepPaperNote)：单篇深读、结构化笔记、多层读者适配。

- [PaperForge](https://github.com/FeijiangHan/PaperForge)：重建推理、解释机制、压力测试假设。

- [paper-reading-skill](https://github.com/sodalone/paper-reading-skill)：按论文类型匹配证据，使用 Claim–Evidence–Verdict。

- [paper-deep-reading-skill](https://github.com/Eroticoo/paper-deep-reading-skill)：原文锚定，区分事实与推断。

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本文件遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.1.0/)。

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P = [ ('arxiv-1409.1556','Very Deep Convolutional Networks for Large-Scale Image Recognition',['Karen Simonyan','Andrew Zisserman'],2014,'ICLR','classic','deep stacks of small convolutional filters','https://arxiv.org/abs/1409.1556'),

('arxiv-1409.1556','Very Deep Convolutional Networks for Large-Scale Image Recognition',['Karen Simonyan','Andrew Zisserman'],2014,'ICLR','classic','deep stacks of small convolutional filters','https://arxiv.org/abs/1409.1556'),

('arxiv-1512.03385','Deep Residual Learning for Image Recognition',['Kaiming He','Xiangyu Zhang','Shaoqing Ren','Jian Sun'],2015,'CVPR','classic','problem-reformulation;new-primitive','https://arxiv.org/abs/1512.03385'),

('arxiv-1409.4842','Going Deeper with Convolutions',['Christian Szegedy','Wei Liu','Yangqing Jia'],2014,'CVPR','classic','unexpected-simplicity;new-primitive','https://arxiv.org/abs/1409.4842'),

('jmlr-v15/srivastava14a','Dropout: A Simple Way to Prevent Neural Networks from Overfitting',['Nitish Srivastava','Geoffrey Hinton','Alex Krizhevsky','Ilya Sutskever','Ruslan Salakhutdinov'],2014,'JMLR','classic','unexpected-simplicity;new

('arxiv-1502.03167','Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift',['Sergey Ioffe','Christian Szegedy'],2015,'ICML','classic','new-primitive;assumption-revisit','https://arxiv.org/abs/1502.031

('arxiv-1412.6980','Adam: A Method for Stochastic Optimization',['Diederik P. Kingma','Jimmy Ba'],2014,'ICLR','classic','new-primitive;unexpected-simplicity','https://arxiv.org/abs/1412.6980'),

('arxiv-1406.2661','Generative Adversarial Nets',['Ian Goodfellow','Jean Pouget-Abadie','Mehdi Mirza','Bing Xu','David Warde-Farley','Sherjil Ozair','Aaron Courville','Yoshua Bengio'],2014,'NeurIPS','classic','new-primitive;problem-reformul

('arxiv-1301.3781','Efficient Estimation of Word Representations in Vector Space',['Tomas Mikolov','Kai Chen','Greg Corrado','Jeffrey Dean'],2013,'NeurIPS workshop','classic','unexpected-simplicity;hidden-equivalence','https://arxiv.org/abs

('arxiv-1409.3215','Sequence to Sequence Learning with Neural Networks',['Ilya Sutskever','Oriol Vinyals','Quoc V. Le'],2014,'NeurIPS','classic','problem-reformulation;new-primitive','https://arxiv.org/abs/1409.3215'),

('arxiv-1706.03762','Attention Is All You Need',['Ashish Vaswani','Noam Shazeer','Niki Parmar','Jakob Uszkoreit','Llion Jones','Aidan N. Gomez','Lukasz Kaiser','Illia Polosukhin'],2017,'NeurIPS','classic','problem-reformulation;new-primitiv

('arxiv-2111.06377','Masked Autoencoders Are Scalable Vision Learners',['Kaiming He','Xinlei Chen','Saining Xie','Yanghao Li','Piotr Dollár','Ross Girshick'],2021,'CVPR','rising-star','unexpected-simplicity;problem-reformulation','https://a

('arxiv-2205.14135','FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness',['Tri Dao','Daniel Y. Fu','Stefano Ermon','Atri Rudra','Christopher Ré'],2022,'NeurIPS','rising-star','new-primitive;cross-domain-transfer','h

('arxiv-2312.00752','Mamba: Linear-Time Sequence Modeling with Selective State Spaces',['Albert Gu','Tri Dao'],2023,'COLM','rising-star','new-primitive;problem-reformulation','https://arxiv.org/abs/2312.00752'),

('arxiv-2304.07193','DINOv2: Learning Robust Visual Features without Supervision',['Maxime Oquab','Timothée Darcet','Théo Moutakanni','Huy V. Vo','Marc Szafraniec','et al.'],2023,'TMLR','rising-star','new-primitive;cross-domain-transfer','h

('arxiv-2501.12948','DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning',['DeepSeek-AI'],2025,'arXiv','rising-star','problem-reformulation;new-primitive','https://arxiv.org/abs/2501.12948'),

('arxiv-2203.02155','Training language models to follow instructions with human feedback',['Long Ouyang','Jeff Wu','Xu Jiang','Diogo Almeida','Carroll Wainwright','et al.'],2022,'NeurIPS','rising-star','new-measurement;new-primitive','https

('arxiv-2103.00020','Learning Transferable Visual Models From Natural Language Supervision',['Alec Radford','Jong Wook Kim','Chris Hallacy','Aditya Ramesh','Gabriel Goh','et al.'],2021,'ICML','community','cross-domain-transfer;problem-refor

('arxiv-2006.11239','Denoising Diffusion Probabilistic Models',['Jonathan Ho','Ajay Jain','Pieter Abbeel'],2020,'NeurIPS','community','problem-reformulation;hidden-equivalence','https://arxiv.org/abs/2006.11239'),

('arxiv-2106.09685','LoRA: Low-Rank Adaptation of Large Language Models',['Edward J. Hu','Yelong Shen','Phillip Wallis','Zeyihao Liu','Yelong Wang','Weizhu Chen'],2021,'ICLR','community','unexpected-simplicity;new-primitive','https://arxiv.

('arxiv-2005.14165','Language Models are Few-Shot Learners',['Tom B. Brown','Benjamin Mann','Nick Ryder','Melanie Subbiah','Jared Kaplan','et al.'],2020,'NeurIPS','community','problem-reformulation;new-measurement','https://arxiv.org/abs/20

('arxiv-2210.03629','ReAct: Synergizing Reasoning and Acting in Language Models',['Shunyu Yao','Jason Zhao','Dian Yu','Nan Du','Izhak Shafran','Karthik Narasimhan','Yuan Cao'],2022,'ICLR','hot','new-primitive;cross-domain-transfer','https:/

('arxiv-2201.11903','Chain-of-Thought Prompting Elicits Reasoning in Large Language Models',['Jason Wei','Xuezhi Wang','Dale Schuurmans','Maarten Bosma','Brian Ichter','et al.'],2022,'NeurIPS','hot','problem-reformulation;unexpected-simplic

('arxiv-2001.08361','Scaling Laws for Neural Language Models',['Jared Kaplan','Sam McCandlish','Tom Henighan','Tom B. Brown','Benjamin Chess','et al.'],2020,'arXiv','hot','new-measurement;assumption-revisit','https://arxiv.org/abs/2001.0836

('arxiv-2305.10601','Tree of Thoughts: Deliberate Problem Solving with Large Language Models',['Shunyu Yao','Dian Yu','Jeffrey Zhao','Izhak Shafran','Tom Griffiths','Yuan Cao'],2023,'NeurIPS','hot','new-primitive;problem-reformulation','htt

('arxiv-2010.11929','An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale',['Alexey Dosovitskiy','Lucas Beyer','Alexander Kolesnikov','Dirk Weissenborn','Xiaohua Zhai','et al.'],2021,'ICLR','top-venue','problem-reformu

('arxiv-2103.14030','Swin Transformer: Hierarchical Vision Transformer using Shifted Windows',['Ze Liu','Yutong Lin','Yue Cao','Han Hu','Yixuan Wei','et al.'],2021,'ICCV','top-venue','new-primitive;cross-domain-transfer','https://arxiv.org/

('arxiv-1810.04805','BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding',['Jacob Devlin','Ming-Wei Chang','Kenton Lee','Kristina Toutanova'],2018,'NAACL','top-venue','problem-reformulation;new-primitive','https

('arxiv-1806.07366','Neural Ordinary Differential Equations',['Ricky T. Q. Chen','Yulia Rubanova','Jesse Bettencourt','David Duvenaud'],2018,'NeurIPS','top-venue','hidden-equivalence;cross-domain-transfer','https://arxiv.org/abs/1806.07366'

('arxiv-2002.05709','A Simple Framework for Contrastive Learning of Visual Representations',['Ting Chen','Simon Kornblith','Mohammad Norouzi','Geoffrey Hinton'],2020,'ICML','top-venue','unexpected-simplicity;new-primitive','https://arxiv.or

('arxiv-2011.13456','Score-Based Generative Modeling through Stochastic Differential Equations',['Yang Song','Jascha Sohl-Dickstein','Diederik P. Kingma','Abhishek Kumar','Stefano Ermon','Ben Poole'],2021,'ICLR','top-venue','hidden-equivale

#!/usr/bin/env python3

#!/usr/bin/env python3

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),

and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

- Issue Tracker: [GitHub Issues](https://github.com/your-repo/issues)

- Discussions: [GitHub Discussions](https://github.com/your-repo/discussions)

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base_url: "https://api.semanticscholar.org/graph/v1"

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base_url: "https://api.openalex.org"

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crossref:     base_url: "https://api.crossref.org"     timeout: 10

base_url: "https://api.crossref.org"

journal = {Nutrients},   doi = {10.3390/nu16131994},   url = {https://openalex.org/W4399979358}

doi = {10.3390/nu16131994},   url = {https://openalex.org/W4399979358} }

url = {https://openalex.org/W4399979358}

journal = {International Journal of Cancer},   doi = {10.1002/ijc.34904},   url = {https://openalex.org/W4396589609}

doi = {10.1002/ijc.34904},   url = {https://openalex.org/W4396589609} }

url = {https://openalex.org/W4396589609}

journal = {Clinical Nutrition},   doi = {10.1016/j.clnu.2024.09.021},   url = {https://openalex.org/W4402469717}

doi = {10.1016/j.clnu.2024.09.021},   url = {https://openalex.org/W4402469717} }

url = {https://openalex.org/W4402469717}

journal = {British Journal Of Nutrition},   doi = {10.1017/s0007114514004139},   url = {https://openalex.org/W2137866867}

doi = {10.1017/s0007114514004139},   url = {https://openalex.org/W2137866867} }

url = {https://openalex.org/W2137866867}

journal = {Nutrients},   doi = {10.3390/nu17071152},   url = {https://openalex.org/W4408905557}

doi = {10.3390/nu17071152},   url = {https://openalex.org/W4408905557} }

url = {https://openalex.org/W4408905557}

journal = {Public Health Nutrition},   doi = {10.1017/s1368980021003256},   url = {https://openalex.org/W3189520656}

doi = {10.1017/s1368980021003256},   url = {https://openalex.org/W3189520656} }

url = {https://openalex.org/W3189520656}

journal = {American Journal of Kidney Diseases},   doi = {10.1053/j.ajkd.2023.09.020},   url = {https://openalex.org/W4389743990}

doi = {10.1053/j.ajkd.2023.09.020},   url = {https://openalex.org/W4389743990} }

url = {https://openalex.org/W4389743990}

journal = {Current Developments in Nutrition},   doi = {10.1016/j.cdnut.2023.102065},   url = {https://openalex.org/W4389975587}

doi = {10.1016/j.cdnut.2023.102065},   url = {https://openalex.org/W4389975587} }

url = {https://openalex.org/W4389975587}

year = {2015},   journal = {PubMed},   url = {https://openalex.org/W2408111232}

journal = {PubMed},   url = {https://openalex.org/W2408111232} }

url = {https://openalex.org/W2408111232}

journal = {Addiction},   doi = {10.1111/add.15684},   url = {https://openalex.org/W3207106585}

doi = {10.1111/add.15684},   url = {https://openalex.org/W3207106585} }

url = {https://openalex.org/W3207106585}

journal = {PLoS ONE},   doi = {10.1371/journal.pone.0190033},   url = {https://openalex.org/W2782215249}

doi = {10.1371/journal.pone.0190033},   url = {https://openalex.org/W2782215249} }

url = {https://openalex.org/W2782215249}

journal = {Journal of Translational Medicine},   doi = {10.1186/s12967-023-04522-8},   url = {https://openalex.org/W4387093948}

doi = {10.1186/s12967-023-04522-8},   url = {https://openalex.org/W4387093948} }

url = {https://openalex.org/W4387093948}

journal = {BMC Medicine},   doi = {10.1186/s12916-021-02024-2},   url = {https://openalex.org/W3182198165}

doi = {10.1186/s12916-021-02024-2},   url = {https://openalex.org/W3182198165} }

url = {https://openalex.org/W3182198165}

journal = {Journal of Public Health},   doi = {10.1007/s10389-023-02074-1},   url = {https://openalex.org/W4386590549}

doi = {10.1007/s10389-023-02074-1},   url = {https://openalex.org/W4386590549} }

url = {https://openalex.org/W4386590549}

journal = {Public Health Nutrition},   doi = {10.1017/s1368980017000234},   url = {https://openalex.org/W2603592506}

doi = {10.1017/s1368980017000234},   url = {https://openalex.org/W2603592506} }

url = {https://openalex.org/W2603592506}

journal = {British Journal of Sports Medicine},   doi = {10.1136/bjsports-2021-104961},   url = {https://openalex.org/W4223614111}

doi = {10.1136/bjsports-2021-104961},   url = {https://openalex.org/W4223614111} }

url = {https://openalex.org/W4223614111}

journal = {European Journal of Nutrition},   doi = {10.1007/s00394-023-03317-3},   url = {https://openalex.org/W4390776370}

doi = {10.1007/s00394-023-03317-3},   url = {https://openalex.org/W4390776370} }

url = {https://openalex.org/W4390776370}

journal = {American Journal of Clinical Nutrition},   doi = {10.1016/j.ajcnut.2025.04.004},   url = {https://openalex.org/W4409359497}

doi = {10.1016/j.ajcnut.2025.04.004},   url = {https://openalex.org/W4409359497} }

url = {https://openalex.org/W4409359497}

journal = {Journal of Activity Sedentary and Sleep Behaviors},   doi = {10.1186/s44167-023-00026-4},   url = {https://openalex.org/W4386399391}

doi = {10.1186/s44167-023-00026-4},   url = {https://openalex.org/W4386399391} }

url = {https://openalex.org/W4386399391}

journal = {European Heart Journal},   doi = {10.1093/eurheartj/ehr158},   url = {https://openalex.org/W1972661406}

doi = {10.1093/eurheartj/ehr158},   url = {https://openalex.org/W1972661406} }

url = {https://openalex.org/W1972661406}

journal = {PLoS ONE},   doi = {10.1371/journal.pone.0165797},   url = {https://openalex.org/W2547031880}

doi = {10.1371/journal.pone.0165797},   url = {https://openalex.org/W2547031880} }

url = {https://openalex.org/W2547031880}

journal = {Food \& Function},   doi = {10.1039/d4fo04741a},   url = {https://openalex.org/W4407588282}

doi = {10.1039/d4fo04741a},   url = {https://openalex.org/W4407588282} }

url = {https://openalex.org/W4407588282}

journal = {Food \& Function},   doi = {10.1039/d4fo05107f},   url = {https://openalex.org/W4406901603}

doi = {10.1039/d4fo05107f},   url = {https://openalex.org/W4406901603} }

url = {https://openalex.org/W4406901603}

journal = {Food \& Function},   doi = {10.1039/d2fo02628g},   url = {https://openalex.org/W4319297822}

doi = {10.1039/d2fo02628g},   url = {https://openalex.org/W4319297822} }

url = {https://openalex.org/W4319297822}

journal = {The Lancet},   doi = {10.1016/s0140-6736(22)02241-3},   url = {https://openalex.org/W4309908979}

doi = {10.1016/s0140-6736(22)02241-3},   url = {https://openalex.org/W4309908979} }

url = {https://openalex.org/W4309908979}

journal = {PLoS Medicine},   doi = {10.1371/journal.pmed.1002039},   url = {https://openalex.org/W2430898493}

doi = {10.1371/journal.pmed.1002039},   url = {https://openalex.org/W2430898493} }

url = {https://openalex.org/W2430898493}

journal = {American Journal of Clinical Nutrition},   doi = {10.1093/ajcn/nqac122},   url = {https://openalex.org/W4282941462}

doi = {10.1093/ajcn/nqac122},   url = {https://openalex.org/W4282941462} }

url = {https://openalex.org/W4282941462}

journal = {Nicotine \& Tobacco Research},   doi = {10.1093/ntr/ntw140},   url = {https://openalex.org/W2407654663}

doi = {10.1093/ntr/ntw140},   url = {https://openalex.org/W2407654663} }

url = {https://openalex.org/W2407654663}

journal = {The Lancet Planetary Health},   doi = {10.1016/s2542-5196(24)00143-8},   url = {https://openalex.org/W4401381375}

doi = {10.1016/s2542-5196(24)00143-8},   url = {https://openalex.org/W4401381375} }

url = {https://openalex.org/W4401381375}

journal = {International Journal of Cancer},   doi = {10.1002/ijc.31593},   url = {https://openalex.org/W2802448066}

doi = {10.1002/ijc.31593},   url = {https://openalex.org/W2802448066} }

url = {https://openalex.org/W2802448066}

journal = {BMC Gastroenterology},   doi = {10.1186/s12876-024-03475-6},   url = {https://openalex.org/W4404147773}

doi = {10.1186/s12876-024-03475-6},   url = {https://openalex.org/W4404147773} }

url = {https://openalex.org/W4404147773}

journal = {Journal of the Academy of Nutrition and Dietetics},   doi = {10.1016/j.jand.2024.11.014},   url = {https://openalex.org/W4404734886}

doi = {10.1016/j.jand.2024.11.014},   url = {https://openalex.org/W4404734886} }

url = {https://openalex.org/W4404734886}

journal = {BMC Medicine},   doi = {10.1186/s12916-022-02619-3},   url = {https://openalex.org/W4308016816}

doi = {10.1186/s12916-022-02619-3},   url = {https://openalex.org/W4308016816} }

url = {https://openalex.org/W4308016816}