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.
中文简介正在准备中。当前显示原文说明。
Use when targeting Communications Earth & Environment or deciding whether an earth, environmental, or planetary-science manuscript fits this venue. Encodes the journal's fit, the solid-and-complete-advance bar, Nature Portfolio reporting/data standards, house style, official-submission re-check, and desk-reject heuristics.
中文简介正在准备中。当前显示原文说明。
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。
Communications Earth & Environment is the Nature Portfolio open-access journal that
publishes high-quality research from across the earth, environmental, and planetary
sciences. Its defining expectation is a solid, complete, and broadly framed advance:
the work must be technically rigorous and represent a meaningful step for its field, but it
need not clear the flagship-Nature bar of transformative significance that
nature-geoscience and nature-climate-change demand. A result that is sound and useful
to its specialist community but framed only for that community — with no articulated
interest for the wider earth-and-environment readership — is a weak fit. 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 Communications Earth & Environment
submission guidance.
nature-geoscience, nature-climate-change) and needs a realistic-tier decision.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Nature Portfolio anchors, then
cite the current Communications Earth & Environment page you checked.nature-geoscience.nature-climate-change.geology or geophysical-research-letters.earth-system-science-data.science-advances.[Fit] High / Medium / Low (one-line reason)
[Target] Communications Earth & Environment
[Topic tags] <2–3 closest earth/environment topics>
[Completeness] <is this a finished, self-contained advance?>
[Broad relevance] <why it matters beyond the immediate specialty>
[Method/evidence] <does specialist-level rigor + data deposition clear the bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / reporting summary / data policy / OA terms / 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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。