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 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.
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。
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>
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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。