Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Use when targeting Environmental Health Perspectives or deciding whether an environmental-health manuscript fits this venue. Encodes the journal's fit, the public-health-relevance and methodological-rigor bar, epidemiological/toxicological expectations, house style, official-submission re-check, and desk-reject heuristics.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Environmental Health Perspectives (EHP) is a peer-reviewed environmental-health journal published with support from the U.S. National Institute of Environmental Health Sciences (NIEHS), connecting environmental exposures to human health with strong public-health and policy relevance. Its defining expectation is public-health significance backed by rigorous epidemiology or toxicology: a study whose findings inform protection of human health and sit within a public-health and regulatory context. Relative to Environment International's broader environmental-science-plus-health scope, EHP is framed around the population-health and policy mission and holds an explicit standard for transparency and reproducibility. A purely environmental-chemistry or mechanistic study with no clear human-health and public-health implication is a weak fit. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidelines. Before submitting, re-check the live EHP submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the official-source anchors, then cite
the current EHP page you checked.environment-international.environmental-pollution.the-lancet.nature-sustainability.environmental-science-and-technology.[Fit] High / Medium / Low (one-line reason)
[Target] Environmental Health Perspectives
[Topic tags] <2–3 closest environmental-health topics>
[Public-health significance] <how the finding informs human-health protection / policy>
[Method/evidence] <does epi/tox rigor + transparency + proportionate claims clear EHP's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / reporting guideline / ethics / data-reproducibility policy / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
中文简介正在准备中。当前显示原文说明。
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
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Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
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Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
中文简介正在准备中。当前显示原文说明。