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 Earth and Planetary Science Letters (EPSL) or deciding whether a quantitative solid-earth or planetary-science manuscript fits this venue. Encodes the journal's broad-interest process-science fit, the quantitative geochemistry/geophysics evidence bar, Elsevier house style, 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。
Earth and Planetary Science Letters (EPSL), published by Elsevier, is a broad-interest journal for quantitative research on the physical and chemical processes of the Earth and other planets. The defining expectation is a process-oriented, quantitatively rigorous advance — in geochemistry, geophysics, geodynamics, petrology and mineral physics, planetary processes, or paleoclimate/paleoceanography proxy science — that is significant to a wide earth- and planetary-science readership rather than to one specialty alone. Despite the "Letters" name, EPSL favors complete, self-contained articles in which the data, modeling, and interpretation jointly support a generalizable mechanism; a purely descriptive regional report, a method note with no process payoff, or a result of interest only to a narrow sub-field is a weak fit. 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 EPSL / Elsevier author instructions and data policy.
geology
(short, high-immediacy GSA letter).../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier/EPSL anchors, then cite
the current EPSL page you checked.geology.nature-geoscience.global-biogeochemical-cycles.earth-system-science-data.geophysical-research-letters.[Fit] High / Medium / Low (one-line reason)
[Target] Earth and Planetary Science Letters
[Topic tags] <2–3 closest solid-earth/planetary topics>
[Process contribution] <the quantitative, generalizable mechanism beyond one locality>
[Method/evidence] <do analytics + uncertainty + model/data evaluation clear EPSL's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / data policy / analytical reporting / 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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