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 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.
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。
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>
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。