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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when targeting Nature Plants or deciding whether a plant-science manuscript fits this venue. Encodes the journal's fit, the broad-significance and Nature-framing bar, evidence and reproducibility expectations, house style, official-submission re-check, and desk-reject heuristics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Nature Plants is the Nature Portfolio journal devoted to plant science in its full breadth — from molecular and cell biology through development, physiology, genetics, and evolution to ecology and crop science — including the plant-associated microbes and the agricultural and societal contexts in which plants matter. Its defining expectation is a significant advance with broad significance: a finding that changes how a wide plant-science readership thinks about a problem, not a clean but incremental result that extends one specialist literature. A technically sound, narrowly framed specialist paper with no broad significance is a poor fit, however careful the work. 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 Nature Plants submission guidance.
the-plant-cell or new-phytologist.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Nature Portfolio anchors, then
cite the current Nature Plants page you checked.the-plant-cell or the-plant-journal.new-phytologist.field-crops-research.nature-food.nature-ecology-and-evolution or nature-communications.[Fit] High / Medium / Low (one-line reason)
[Target] Nature Plants
[Topic tags] <2–3 closest plant-science topics>
[Broad significance] <why it matters across plant science, not one subfield>
[Method/evidence] <does convergent evidence + reproducibility clear Nature Plants' bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / reporting summary / data + accession policy / 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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.