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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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。
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 requirements还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。