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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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>
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.