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 targeting Trends in Food Science & Technology or deciding whether a food-science review manuscript fits this venue. Encodes the journal's review-only fit, the critical-synthesis and comprehensiveness bar, structure expectations, official-submission re-check, and desk-reject heuristics.
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。
Trends in Food Science & Technology is the Elsevier review and synthesis journal for food science and technology — critical reviews, trend analyses, and opinion that frame the state and direction of a field. Its defining expectation is critical synthesis, not new primary data: a comprehensive, evaluative account that integrates the existing literature, identifies what is settled and what is contested, and points to research gaps and emerging directions. A manuscript whose core contribution is original experimental results is the single largest desk-reject risk here, regardless of quality. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidelines. Before submitting, re-check the live Trends in Food Science & Technology guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Trends in Food Science & Technology page you checked.food-chemistry.nature-food.journal-of-cleaner-production.annual-review-of-environment-and-resources.environmental-pollution.[Fit] High / Medium / Low (one-line reason)
[Target] Trends in Food Science & Technology
[Topic tags] <2–3 closest food-science review topics>
[Synthesis contribution] <the framework / reconciliation / gap-mapping the review delivers>
[Review rigor] <coverage, balance, and (if systematic) search transparency>
[Top risk] <the single most likely reason for rejection — usually "this is primary research">
[Official items to re-check] <article type / review reporting standard / highlights / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>
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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。