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 Agricultural and Forest Meteorology or deciding whether a land–atmosphere manuscript fits this venue. Encodes the journal's fit, the land–atmosphere-process and flux-measurement bar, micrometeorological evidence expectations, Elsevier 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.
Agricultural and Forest Meteorology is Elsevier's outlet for the interaction between agricultural and forest ecosystems and the atmosphere: the land–atmosphere exchange of energy, water, carbon, and trace gases; micrometeorology and boundary-layer processes; eddy-covariance and other flux measurement; phenology; and crop- and forest–climate interactions. Its defining expectation is a genuine land–atmosphere or biosphere–atmosphere process contribution — an advance in how vegetated surfaces exchange mass and energy with the atmosphere, or in how that exchange is measured, modeled, or scaled. A pure crop-agronomy study with no atmospheric coupling, or a pure climate-modeling paper with no surface-process component, is a poor fit however competent. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidance. Before submitting, re-check the live Agricultural and Forest Meteorology author instructions and data policy.
agriculture-ecosystems-and-environment,
global-change-biology, and a climate venue.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Agricultural and Forest Meteorology page you checked.agriculture-ecosystems-and-environment.global-change-biology.journal-of-hydrology.global-biogeochemical-cycles.journal-of-climate or nature-climate-change.[Fit] High / Medium / Low (one-line reason)
[Target] Agricultural and Forest Meteorology
[Topic tags] <2–3 closest land–atmosphere topics>
[Land–atmosphere process] <the energy/water/carbon/trace-gas exchange or micromet advance>
[Method/evidence] <does flux processing + model evaluation + scaling clear the bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / 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.