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 building figures and tables for an Agricultural Systems (AgSy) manuscript so exhibits communicate interactions, dynamics, trade-offs, and model performance clearly. AgSy is a systems journal, so the best exhibits show trade-off frontiers, observed-vs-simulated fit, resource flows, and conceptual system diagrams — not just a bar chart of one treatment. Designs exhibits; it does not run the analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Exhibits are where a systems reviewer checks whether the model and the trade-offs are real. At AgSy the strongest exhibits make interactions, dynamics, and trade-offs visible, and let a reader judge model performance at a glance. A wall of single-treatment bar charts does not belong in a systems paper.
agsy-reproducibility-and-data-policy).A crop–livestock simulation has four candidate main figures; choose by what the systems claim needs:
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.【Main exhibit】what system interaction / trade-off / fit it shows
【Type】trade-off frontier / 1:1 / time series / flow diagram / system diagram / radar
【Self-contained?】caption + labels + units + level + scenario + N? [Y/N]
【Uncertainty shown?】intervals / ensemble / boxplot? [Y/N]
【Article vs supplementary】split decided
【Reproducible?】generated by master workflow, matches package? [Y/N]
【Next】agsy-writing-style
../../resources/external_tools.md — plotting, flow-diagram, and mapping tools for systems exhibits../../resources/official-source-map.md — Highlights / graphical-abstract requirements아직 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.