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 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.
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。
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 policy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。