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 the Journal of Cleaner Production or deciding whether a cleaner-production/sustainability manuscript fits this venue. Encodes the journal's fit, the cleaner-production contribution and methodological-transparency bar, LCA/system-boundary 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.
The Journal of Cleaner Production (JCLP) is the Elsevier journal for cleaner production, sustainability, and the transition to sustainable systems — cleaner technologies and processes, circular-economy strategies, sustainability assessment (including life-cycle assessment), and corporate and industrial sustainability. Its defining expectation is a cleaner-production or sustainability-transition contribution with transparent method: a study that demonstrably reduces environmental burden or advances sustainable practice, with explicit assumptions, system boundaries, and data sources. Relative to Resources, Conservation & Recycling, JCLP is broader — it spans technology, management, policy, and assessment rather than focusing on material/waste circularity. A descriptive case study with opaque LCA assumptions or no generalizable cleaner-production insight 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 JCLP submission guidance.
../../resources/source-basis.md and
../../resources/official-source-map.md; start from the Elsevier anchors, then cite the
current Journal of Cleaner Production page you checked.resources-conservation-and-recycling.trends-in-food-science-and-technology (food) or annual-review-of-environment-and-resources (environment).environmental-pollution.environmental-science-and-technology.nature-sustainability.[Fit] High / Medium / Low (one-line reason)
[Target] Journal of Cleaner Production
[Topic tags] <2–3 closest cleaner-production/sustainability topics>
[Cleaner-production contribution] <the generalizable advance beyond the case>
[Method/transparency] <are boundaries/assumptions/inventory explicit and sensitivity-tested?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / LCA reporting / highlights / data policy / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.