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.
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Use when targeting the Annual Review of Environment and Resources or deciding whether an environment-and-resources synthesis fits this invited-review venue. Encodes the journal's invited-review fit, the authoritative-synthesis bar, structure expectations, official-submission re-check, and desk-reject heuristics.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
The Annual Review of Environment and Resources (AREAR) is the Annual Reviews venue for authoritative, largely invited review articles that synthesize fields across environment and natural resources — climate, energy, water, land, biodiversity, ecosystems, pollution, and the human and institutional dimensions of resource use. Its defining expectation is a definitive, field-defining synthesis by recognized experts: a critical, balanced account that consolidates a body of literature, frames the state of knowledge, and sets the agenda — not primary research and not a routine literature review. Because most articles are commissioned, an uninvited primary-research submission is the single largest misfit. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidelines or its editorial/commissioning process. Before submitting or proposing, re-check the live AREAR guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the official-source anchors, then cite
the current AREAR page you checked.trends-in-food-science-and-technology.environmental-pollution, global-change-biology, or a method venue.journal-of-cleaner-production.nature-sustainability.science-advances review format.[Fit] High / Medium / Low (one-line reason)
[Target] Annual Review of Environment and Resources
[Topic tags] <2–3 closest environment-and-resources topics>
[Synthesis contribution] <the field-defining, agenda-setting integration delivered>
[Invitation status] <commissioned / proposal pathway / uninvited — the key gate>
[Top risk] <the single most likely reason for rejection — usually "primary research / uninvited">
[Official items to re-check] <commissioning process / length / summary-points format / 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.
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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.
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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.
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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.
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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.
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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.
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