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 positioning an Agricultural Systems (AgSy) manuscript against the literature so it reads as a systems contribution. AgSy readers span agronomy, modelling, livestock, economics, environment, and food-system science, so the paper must engage the systems and modelling literatures they expect — not only one subfield. Stakes the contribution; it does not write the lit review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
AgSy is read by a systems-science audience that spans crop and livestock modelling, whole-farm and bio-economic analysis, sustainability assessment, and food-system science. Positioning is therefore not throat-clearing — the paper must show it advances the systems and modelling conversation, not just add another data point to one subfield.
agsy-data-and-model-evaluation).| If your paper is… | also engage… |
|---|---|
| a crop-model application | the whole-farm / trade-off literature it feeds into |
| a whole-farm or bio-economic study | the process-model and the economic/decision literatures |
| a landscape/regional analysis | the aggregation/emergence and land-use-competition literature |
| a food-system study | the production, environment, and policy literatures it connects |
A bioeconomic study of climate adaptation positions itself in two literatures at once:
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.【Systems debate】the open trade-off / mechanism / scaling / model gap
【Key works】the 3-6 that define it (domain + methods)
【Model landscape】where your model sits among alternatives
【Gap】what is mismodelled / untested across scales / missing a trade-off
【Move】how this paper changes the systems conversation
【Strongest rival】and how evaluation will adjudicate it
【Next】agsy-systems-framing-and-modeling
../../resources/external_tools.md — model families and systems data sources../../resources/official-source-map.md — AgSy scope and interaction-centered remit아직 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.