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 Environment International or deciding whether an environmental-exposure/health manuscript fits this venue. Encodes the journal's fit, the population-relevant exposure-health evidence bar, exposome/contaminant 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。
Environment International is the Elsevier journal for environmental science with a strong human-health and exposure emphasis — environmental exposures and contaminants, the exposome, environmental epidemiology, and human health-risk assessment across air, water, soil, food, and the built environment. Its defining expectation is population-relevant exposure–health evidence: a study that connects a measured or modeled environmental exposure to a human health outcome or risk with rigorous quantification. Compared with Environmental Health Perspectives, the scope is broader environmental-science-plus-health and method-forward; a purely environmental-monitoring paper with no health relevance, or a clinical study with no environmental exposure, 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 Environment International submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Environment International page you checked.environmental-health-perspectives.environmental-pollution.food-chemistry.nature-sustainability.the-lancet or a dedicated epidemiology journal.[Fit] High / Medium / Low (one-line reason)
[Target] Environment International
[Topic tags] <2–3 closest exposure/health topics>
[Exposure–health link] <the exposure measure and the human outcome/risk it connects to>
[Method/evidence] <does confounding control + exposure rigor + uncertainty clear the bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / epidemiology reporting / ethics / data 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.
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