「evidence」の検索結果

Use when building exhibits for an American Anthropologist (AA) manuscript — photographs, maps, site plans, artifact images, kinship diagrams, transcripts, evidence tables, and multimodal media — so they are self-contained, accessible (alt text), ethically cleared (consent, permissions), and count toward the word budget. Designs exhibits; it does not run analysis or clear permissions for you.

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 deciding which English agriculture / environment / earth-science journal skill to invoke next, comparing fit across the 30-journal agriculture-and-environment roadmap, or routing a manuscript before venue-specific re-framing. Routes by sub-field (agronomy / plant / soil / food / environment / earth / climate), contribution type, and evidence shape (field trial / lab assay / observational survey / model / dataset).

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 Environment International or deciding whether an environmental-exposure/health manuscript fits this venue. Encodes the journal's fit, the population-relevant exposure-health evidence bar, exposome/contaminant expectations, house style, official-submission re-check, and desk-reject heuristics.

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 Environmental Pollution or deciding whether a contamination/pollutant-impact manuscript fits this venue. Encodes the journal's fit, the source-fate-impact evidence bar, measurement-and-controls expectations, house style, official-submission re-check, and desk-reject heuristics.

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 Global Change Biology or deciding whether a biology/ecology manuscript fits this venue. Encodes the journal's fit, the global-change-driver framing and biological-evidence bar, scale and mechanism expectations, house style, official-submission re-check, and desk-reject heuristics.

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 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.

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 Annual Review of Plant Biology or deciding whether a plant-biology review proposal fits this invited-review venue. Encodes the journal's fit, the authoritative-synthesis and invited-article bar, evidence and balance expectations, house style, official-submission re-check, and desk-reject heuristics.

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 Agricultural and Forest Meteorology or deciding whether a land–atmosphere manuscript fits this venue. Encodes the journal's fit, the land–atmosphere-process and flux-measurement bar, micrometeorological evidence expectations, Elsevier house style, official-submission re-check, and desk-reject heuristics.

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 Earth and Planetary Science Letters (EPSL) or deciding whether a quantitative solid-earth or planetary-science manuscript fits this venue. Encodes the journal's broad-interest process-science fit, the quantitative geochemistry/geophysics evidence bar, Elsevier house style, official-submission re-check, and desk-reject heuristics.

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 packaging code, models, datasets, or media as ACM MM (ACM Multimedia) artifacts — building the anonymous review package versus the public release, and choosing between the Open Source Software Competition, the Dataset track, the Reproducibility track, and main-track supplementary evidence, each with its own blinding and expectations.

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 the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.

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 executing and reporting the analysis for an Administrative Science Quarterly (ASQ) manuscript — qualitative coding and data-to-theory construction, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see asq-methods).

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 revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.

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 building the exhibits of an Accounting, Organizations and Society (AOS) manuscript — data-inventory and evidence tables for qualitative work, cell-means and process-test tables for experiments, and figures that carry theoretical weight — in Elsevier-compatible format. Builds exhibits; it does not run the analysis (aos-data-analysis).

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 polishing the prose of an Accounting, Organizations and Society (AOS) manuscript — sustained argumentative writing that carries theory and evidence together, a self-contained abstract, disciplined length despite the absence of a hard cap, and full anonymization for double-anonymized review. Polishes prose; it does not run the submission preflight (aos-submission).

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 reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.

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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.

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。

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 organizing AAAI supplementary material, including the technical appendix, multimedia appendix, and code/data ZIPs, while respecting that AAAI supplements are due with the paper, treated as immutable after submission, must stay double-blind, and should never hide main-paper-critical evidence from reviewers who skim the appendix.

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 packaging AAMAS multiagent code, environments, opponent and population sets, random seeds, game definitions, and logs as anonymous supplementary evidence or a public post-acceptance release, even without a separate artifact badge, so that game-theory and MARL reviewers can inspect and re-run the interaction claims.

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 AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.

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 AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

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。

Find and evaluate creators, influencers, affiliates, and UGC partners using audience fit, content fit, authentic product adjacency, engagement quality, public contactability, commerce signals, and brand-safety evidence—not follower count alone.

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。