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 Soil Biology & Biochemistry or deciding whether a soil-science manuscript fits this venue. Encodes the journal's fit, the soil-process-mechanism bar, evidence and data-reporting 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。
Soil Biology & Biochemistry (SBB), published by Elsevier, is the leading journal for the mechanisms of soil biological and biochemical processes: soil organic matter dynamics, microbial ecology and function, nutrient cycling, soil carbon, extracellular enzymes, and the biogeochemical processes that soil biota drive. Its defining expectation is mechanistic process understanding — evidence about how soil organisms and reactions govern a process, not a descriptive inventory of what is present. A soil survey, a descriptive community census with no process link, or a management trial whose contribution is an agronomic outcome rather than a soil mechanism is a poor 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 SBB author guidance.
agriculture-ecosystems-and-environment (agroecosystem
environmental outcomes), and a broader biogeochemistry venue.../../resources/source-basis.md and
../../resources/official-source-map.md; start from the official source anchors, then
cite the current Soil Biology & Biochemistry page you checked.agriculture-ecosystems-and-environment.field-crops-research.agronomy-for-sustainable-development.global-change-biology.nature-communications or a global-biogeochemistry venue.[Fit] High / Medium / Low (one-line reason)
[Target] Soil Biology & Biochemistry
[Topic tags] <2–3 closest soil-process topics>
[Process mechanism] <the soil biota/reaction → process-rate/pool link being established>
[Method/evidence] <does function-linkage + replication + method rigor clear SBB's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / sequence deposition / 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.
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.
中文简介正在准备中。当前显示原文说明。