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.
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Use when targeting Global Biogeochemical Cycles (GBC) or deciding whether a biogeochemical-cycling manuscript fits this venue. Encodes the journal's system-scale fluxes/budgets/feedbacks fit, the quantitative biogeochemistry evidence bar, AGU FAIR data-deposition expectations, house style, official-submission re-check, and desk-reject heuristics.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Global Biogeochemical Cycles (GBC) is the American Geophysical Union's journal for the cycling of carbon, nutrients, and elements through the earth system: the fluxes, budgets, and feedbacks that move and transform elements across land, ocean, and atmosphere, constrained by observations, experiments, and models. The defining expectation is a quantitative, system-scale advance in biogeochemistry — a better-constrained flux or budget, a newly characterized feedback, or a mechanism operating at regional-to-global scale — not a site-level process measurement of interest mainly to one community. A descriptive local incubation, a single-station time series, or an ecological/soil study with no implication for an element budget or earth-system feedback is a weak fit. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidance. Before submitting, re-check the live GBC / AGU author instructions and data policy.
soil-biology-and-biochemistry (soil-process
mechanism), and water-resources-research (water science).../../resources/source-basis.md and
../../resources/official-source-map.md; start from the AGU anchors, then cite the current
GBC page you checked.soil-biology-and-biochemistry.water-resources-research / journal-of-hydrology.global-change-biology.journal-of-climate.earth-system-science-data.[Fit] High / Medium / Low (one-line reason)
[Target] Global Biogeochemical Cycles
[Topic tags] <2–3 closest biogeochemical-cycling topics>
[System-scale contribution] <the flux/budget/feedback advance beyond one site>
[Method/evidence] <do uncertainty + scaling + model/data evaluation + deposition clear GBC's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / key points / AGU data-software 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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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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.
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