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cjc-experiments

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在为投向《计算机学报》(Chinese Journal of Computers, CJC) 的长文设计与呈现实验、评估或理论验证时调用。覆盖如何按贡献类型(算法/系统/理论/实证/学习类)匹配证据、如何设立评估问题与公平基线、如何选数据集与指标、如何做消融与显著性分析、如何报告均值方差与统计检验而非单点最优、如何防数据泄漏与评测污染、以及如何用图表在长文中充分而不冗余地呈现结果。适用于让计算机全学科中文原创研究的实验部分经受本刊三审专家对"数据充分真实、结论可信"的审查。

Chinese-Journal-of-Computers-Skills/skills/cjc-experiments/SKILL.md

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# 《计算机学报》实验设计与呈现

《计算机学报》(Chinese Journal of Computers, CJC) 要求来稿"数据充分、真实"，外审专家会实质性追问
实验是否足以支撑结论。作为 CCF A 类综合性中文长文期刊，CJC 给了充分篇幅展开实验，但也因此对
**证据与主张的匹配度**要求更高。本技能给出面向 CJC 的实验设计与呈现方法。核验日期 2026-07-09，
与 `cjc-reproducibility` 配套使用。

## 一、先立评估问题，再做实验

好的实验部分从**评估问题(RQ)**开始，而非直接堆表格。为每个贡献点设一个可回答的问题，例如：
- RQ1：所提方法在 <任务> 上是否优于 <代表性基线>？优多少？
- RQ2：各模块对性能的贡献如何（消融）？
- RQ3：方法在 <规模/噪声/分布变化> 下是否稳健？
- RQ4：开销（时间/内存/通信）是否可接受？
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