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 writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A revise decision at AgSy is a real chance — but systems reviewers tend to press on the model (calibration, evaluation, sensitivity, uncertainty) and the trade-off / decision framing. The response letter must move every reviewer toward yes while keeping the editor confident the revision converges. Reviewers are not anonymous to you on their side (single anonymized), but keep the tone professional and evidence-based.
agsy-reproducibility-and-data-policy).For each reviewer comment:
> [Quoted reviewer comment]
Response: [What we did / why we respectfully disagree, with a systems reason].
Change: [Section/page/figure-table number; new evaluation/sensitivity run where relevant].
Open with a short summary of the main changes to the editor; group by reviewer; end each entry with the location of every change so the editor can verify quickly.
A crop–livestock simulation gets a major revision. R1 wants three more management scenarios; R2 wants a simpler model and doubts the calibration. The response resolves the conflict openly rather than splitting the difference:
【Editor's decisive points】addressed first? [list]
【Coverage】every reviewer comment answered? [Y/N]
【Concede vs rebut】each tagged with evidence + change location
【Model strengthened】added evaluation/sensitivity/uncertainty where pressed? [Y/N]
【Reviewer conflicts】reconciled and explained to editor? [Y/N]
【Contribution protected】systems framing + trade-off intact? [Y/N]
【Package updated】data/code/model in sync? [Y/N]
【Next】resubmit via Editorial Manager
../../resources/official-source-map.md — review model, reviewer count, editor-decision policy아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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.