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 Agriculture, Ecosystems & Environment or deciding whether an agroecosystem-environment manuscript fits this venue. Encodes the journal's fit, the field-to-landscape environmental-outcome bar, data-reporting 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。
Agriculture, Ecosystems & Environment (AGEE), published by Elsevier, sits at the interface of agriculture and the environment at field-to-landscape scale: how agricultural systems affect — and are affected by — nutrient and greenhouse-gas fluxes, biodiversity, soil and water quality, and ecosystem services. Its defining expectation is a study whose central contribution is an environmental outcome of agroecosystem management, quantified with sound design and generalizable beyond one plot. A pure crop-yield/agronomy study with no environmental outcome, or a soil-process mechanism study with no agroecosystem/management framing, fits a sibling journal better. 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 AGEE author guidance.
field-crops-research (crop/yield focus), and
soil-biology-and-biochemistry (soil-process mechanism).../../resources/source-basis.md and
../../resources/official-source-map.md; start from the official source anchors, then
cite the current Agriculture, Ecosystems & Environment page you checked.field-crops-research.soil-biology-and-biochemistry.agronomy-for-sustainable-development.global-change-biology.nature-food.[Fit] High / Medium / Low (one-line reason)
[Target] Agriculture, Ecosystems & Environment
[Topic tags] <2–3 closest agriculture–environment topics>
[Environmental outcome] <the field-to-landscape outcome and its management driver>
[Method/evidence] <does design + measurement rigor + replication clear AGEE's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / data policy / measurement + metadata reporting / 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。