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 Resources, Conservation & Recycling or deciding whether a resource-efficiency/circular-economy manuscript fits this venue. Encodes the journal's fit, the materials-and-waste circularity contribution bar, material-flow-analysis expectations, house style, 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。
Resources, Conservation & Recycling (RCR) is the Elsevier journal for resource efficiency, waste management, recycling, and the circular economy of materials — material-flow and substance-flow analysis, recycling and resource-recovery technologies and systems, and the conservation and reuse of materials. Its defining expectation is a materials-and-waste circularity contribution: a study that quantifies, improves, or systematizes how materials are conserved, recovered, recycled, or kept in productive use. Relative to the Journal of Cleaner Production's broader cleaner-production/sustainability remit, RCR is anchored in the physical reality of materials and waste flows. A general sustainability-management essay, or a recycling case with no quantified material outcome, is a weak fit. 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 RCR submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Resources, Conservation & Recycling page you checked.journal-of-cleaner-production.environmental-pollution.annual-review-of-environment-and-resources.environmental-science-and-technology.nature-sustainability.[Fit] High / Medium / Low (one-line reason)
[Target] Resources, Conservation & Recycling
[Topic tags] <2–3 closest circular-materials topics>
[Circularity contribution] <the quantified flow/recovery/efficiency result>
[Method/evidence] <are boundaries, mass balance, yields, and uncertainty reported?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / MFA reporting / highlights / data policy / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>
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。