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 Global Change Biology or deciding whether a biology/ecology manuscript fits this venue. Encodes the journal's fit, the global-change-driver framing and biological-evidence bar, scale and mechanism 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.
Global Change Biology is the Wiley journal for the biological and ecological responses of the living world to global change — climate warming, elevated CO2, altered precipitation, nitrogen deposition, land-use and land-cover change, and their interactions. Its defining expectation is a clear global-change driver coupled to rigorous biological evidence: a study that links an explicit global-change forcing to a biological or ecological response, with mechanism or consequence resolved from the molecular to the ecosystem scale. A local ecology, physiology, or biogeochemistry study with no global-change driver or framing is a weak fit, however well executed. 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 Global Change Biology submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Wiley anchors, then cite the
current Global Change Biology page you checked.nature-food.environmental-pollution.annual-review-of-environment-and-resources.nature-sustainability.ecology / journal-of-ecology).[Fit] High / Medium / Low (one-line reason)
[Target] Global Change Biology
[Topic tags] <2–3 closest global-change-biology topics>
[Global-change driver] <the explicit forcing and the biological response it drives>
[Method/evidence] <does replication/attribution/benchmarking clear the biological-evidence bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / data-code policy / meta-analysis standard / 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.