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.
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Use when drafting or polishing an Agricultural Systems (AgSy) manuscript so it reads clearly for a systems-science audience, follows the journal's format, and meets front-matter requirements (abstract <= 250 words, Highlights, graphical abstract). Research papers run to about 8,000 words (no hard cap). Tightens prose and format; it does not invent content.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
An AgSy paper must be readable by a broad systems-science audience — agronomists, modellers, economists, and environmental scientists — and prepared with the journal's front matter. This skill is about communicating a systems analysis clearly and meeting Elsevier's format, not about generating claims.
agsy-systems-framing-and-modeling).elsarticle for LaTeX).agsy-submission).Write each section to answer the question a systems referee raises there. Title: name the system and the trade-off, not just the crop. Abstract: lead with the interaction, end on the decision (≤250 words). Introduction: state boundary, interaction, model, and stakes by the last paragraph. Methods: plain-language model first, equations second. Results/Discussion: lead with the trade-off, then tie it to the decision and match claims to uncertainty.
Before (buries the system): "Simulations were run for several management options and yields compared across scenarios." — passive, no interaction, no trade-off, no decision.
After (systems-first): "A whole-farm bioeconomic model shows that shifting 30% of area to a legume intercrop raises modelled gross margin ~8% but increases nitrogen surplus ~14%, sharpening a margin–environment trade-off advisers must weigh when targeting rotation subsidies." — interaction, quantified trade-off (illustrative), and decision in one sentence.
agsy-literature-positioning).【Systems contribution stated by end of intro?】[Y/N]
【System defined early?】boundary + components + level? [Y/N]
【Abstract】word count (≤250)
【Highlights + graphical abstract】prepared? [Y/N]
【Length】near ~8,000 words; extras moved to supplementary? [Y/N]
【References + declarations】styled + CRediT/COI/funding/AI disclosure ready? [Y/N]
【Next】agsy-impact-and-implications
../../resources/external_tools.md — reference managers and Elsevier typesetting tools../../resources/official-source-map.md — abstract cap, Highlights, graphical abstract, declarations还没有评价。欢迎分享你的使用感受。
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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.
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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.
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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.
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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.
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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.
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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.
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