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 System Science Data (ESSD) or deciding whether an earth-system dataset fits this venue. Encodes the journal's data-paper fit, the open-deposition-with-DOI requirement, quality and reuse-documentation bar, Copernicus 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 System Science Data (ESSD) is the Copernicus / European Geosciences Union venue for data papers: peer-reviewed articles whose primary product is an original, high-quality, openly deposited dataset of lasting value to earth-system science. The defining expectation is not a new scientific conclusion but a reusable dataset: the manuscript documents how the data were produced, calibrated, quality-controlled, and how others should reuse them, while the dataset itself lives in a FAIR public repository with its own persistent identifier (DOI). An analysis or interpretation paper that mines data toward a hypothesis, or a paper whose data are not openly and permanently available, is a fundamental misfit here. 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 ESSD / Copernicus author instructions and data policy.
water-resources-research, global-biogeochemical-cycles) for an analysis paper.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Copernicus/ESSD anchors, then
cite the current ESSD page you checked.communications-earth-and-environment or a domain journal, with ESSD as the data citation.global-biogeochemical-cycles.water-resources-research / journal-of-hydrology.journal-of-climate.earth-and-planetary-science-letters.[Fit] High / Medium / Low (one-line reason)
[Target] Earth System Science Data
[Data product] <what the dataset is and what community reuses it>
[Deposition] <repository + DOI + version + license status>
[Quality/documentation] <does QC + validation + metadata clear ESSD's reuse bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <data-paper structure / FAIR deposition / metadata / open-review / disclosures>
[Re-route suggestion] <if it is an analysis paper, 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。