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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when targeting Environmental Pollution or deciding whether a contamination/pollutant-impact manuscript fits this venue. Encodes the journal's fit, the source-fate-impact evidence bar, measurement-and-controls expectations, house style, official-submission re-check, and desk-reject heuristics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Environmental Pollution is the Elsevier journal for environmental contamination and its effects — the sources, fate, transport, and ecological and health impacts of pollutants across air, water, soil, and biota, including emerging contaminants, microplastics, metals, and persistent organics. Its defining expectation is rigorous evidence of pollutant impact, not mere detection: a study that quantifies a contaminant with sound measurement and controls and connects it to fate, transport, exposure, or an ecological/health effect. A "we detected X in Y" survey with no impact, mechanism, or fate insight — and no quantification beyond presence — 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 Environmental Pollution submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Environmental Pollution page you checked.environment-international.environmental-health-perspectives.global-change-biology.food-chemistry.environmental-science-and-technology.[Fit] High / Medium / Low (one-line reason)
[Target] Environmental Pollution
[Topic tags] <2–3 closest contamination/pollutant topics>
[Impact beyond detection] <the fate / transport / exposure / effect the study establishes>
[Method/evidence] <do QA/QC, sampling design, controls, and dose-response clear the bar?>
[Top risk] <the single most likely reason for rejection — often "detection-only">
[Official items to re-check] <article type / QA/QC reporting / highlights / 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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.