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 to understand how Agricultural Systems (AgSy) evaluates a manuscript — single-anonymized peer review with a minimum of two reviewers and an editor decision, desk screening for fit and systems content, what reviewers weigh, and the rapid-review track for Perspectives. Sets expectations and shapes the paper to survive review; it does not contact editors.
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。
Knowing how AgSy screens and decides lets you pre-empt the failure modes before submitting. AgSy is single anonymized (reviewers see the authors), screens for fit and genuine systems content, and sends suitable papers to a minimum of two expert reviewers; the editor makes the decision. Source-map process facts were refreshed from ScienceDirect on 2026-06-20; live-check them before a real upload because review wording and submission links can change.
Before external review, the editor screens for fit and substance. Map your submission to the gate:
agsy-systems-framing-and-modeling).Run this as a concrete capability pass. First lock the system boundary, actor decision, model/data linkage, and sustainability or food-security tradeoff; then test whether the manuscript addresses agricultural-systems reviewers who expect crop, farm, value-chain, environment, and policy components to be connected rather than listed.
claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.【Fit check】genuine systems question (interactions/trade-offs)? [Y/N]
【Model rigor】described + calibrated + independently evaluated? [Y/N]
【Sensitivity/uncertainty】characterized? [Y/N]
【Decision relevance】trade-offs + application clear? [Y/N]
【Track】standard vs Perspective (rapid review)
【Realistic outcome】reject / major / minor / (rare) accept
【Next】agsy-submission (or agsy-revision-and-rebuttal if decided)
../../resources/official-source-map.md — peer-review model, reviewer count, editor decision, rapid review for Perspectives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。