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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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>
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。