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 deciding whether a project fits Agricultural Systems (AgSy) and which article type to target. AgSy is a systems-science journal, so the test is a genuine systems question — interactions among components, across hierarchical levels (field → farm → landscape → region → food system), trade-offs, and emergent behavior — analyzed with integrated modelling, NOT a single-factor field trial. Helps frame the question; it does not collect data.
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。
Agricultural Systems publishes the systems analysis of agricultural systems. The bar is not "a new result for crop X" — it is "a systems question about interactions and trade-offs." Use this skill to pressure-test fit before you invest, and to keep a field trial from being mistaken for a systems paper.
A strong AgSy paper usually clears all four:
agsy-impact-and-implications).| If your work is at… | reach AgSy fit by… |
|---|---|
| Field / plot | scaling to the farm or showing the field-level interaction that matters to the system |
| Whole-farm | the journal's sweet spot — show component interactions and whole-farm trade-offs |
| Landscape / region | spatial interactions, land-use competition, aggregation/emergence across farms |
| Food system | linking production to value chains, food security, environment, and policy |
agsy-review-process).Score the project against the four fit criteria, then read off the verdict.
| Systems Q | Scope | Trade-offs | Decision | Verdict |
|---|---|---|---|---|
| Yes | Yes | Yes | Yes | Strong AgSy fit — proceed |
| Yes | Field | Yes | Yes | Needs scaling: embed in a farm/landscape system |
| Yes | Yes | One optimum | Yes | Reframe: surface the trade-off across objectives |
| No | — | — | — | Off-fit — re-route to an agronomy/field-crops journal |
| Yes | Yes | Yes | No | Add the decision hook (agsy-impact-and-implications) |
When a project lands near the boundary, route it deliberately:
A team has trial data showing a legume intercrop raises maize yield:
【Question】one sentence (state the interaction / trade-off)
【System & boundary】components + hierarchical level(s) + scale
【Modelling approach】conceptual / empirical / dynamic / bio-economic
【Decision relevance】who acts differently if this is true
【Article type】Research paper / Short communication / Perspective / Comment / Review
【Fit verdict】strong / needs reframing / off-fit (why)
【Next】agsy-literature-positioning
../../resources/external_tools.md — systems models and data sources by scale../../resources/official-source-map.md — AgSy aims, scope, and article types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。