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 Forest Ecology and Management or deciding whether a forest-science manuscript fits this venue. Encodes the journal's fit, the ecological-evidence-with-management-relevance bar, silviculture and disturbance expectations, Elsevier 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。
Forest Ecology and Management is Elsevier's outlet for the science of forest ecosystems and how that science informs their management: forest ecology and stand dynamics, silviculture and regeneration, disturbance (fire, wind, insects, pathogens) and restoration, biodiversity and habitat, and carbon, growth, and productivity. Its defining expectation is sound ecological evidence paired with explicit management relevance — a study must both advance understanding of how forests function and make clear what its findings mean for managing, restoring, or conserving forested land. A competent forest-ecology paper with no applied or management implication, or a non-forest ecological study, is a poor fit however rigorous. 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 Forest Ecology and Management author instructions and data policy.
global-change-biology,
agriculture-ecosystems-and-environment, and a general-ecology venue.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Forest Ecology and Management page you checked.global-change-biology.agriculture-ecosystems-and-environment.global-biogeochemical-cycles.agricultural-and-forest-meteorology.functional-ecology or ecology-letters.[Fit] High / Medium / Low (one-line reason)
[Target] Forest Ecology and Management
[Topic tags] <2–3 closest forest-science topics>
[Ecological evidence] <the stand/disturbance/biodiversity finding and its strength>
[Management relevance] <the explicit management/restoration/conservation implication>
[Method/evidence] <does design + statistics + validation clear the bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / highlights / data policy / permits / 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。