「eval」の検索結果

Use when targeting Journal of Climate or deciding whether a climate-dynamics or climate-variability manuscript fits this venue. Encodes the journal's fit, the large-scale-climate-science bar, model-evaluation and statistical-rigor expectations, AMS house style, official-submission re-check, and desk-reject heuristics.

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Use when you need to understand how American Anthropologist (AA) evaluates a manuscript — editorial screening, anonymous peer review under an ethics of care, the four-field editorial board, decision categories, and how section choice shapes the path. Sets expectations and shapes the paper to survive review; it does not contact editors.

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Use to understand how Agricultural Systems (AgSy) evaluates a manuscript — single-anonymized peer review with a minimum of two reviewers and an editor decision, desk screening for fit and systems content, what reviewers weigh, and the rapid-review track for Perspectives. Sets expectations and shapes the paper to survive review; it does not contact editors.

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Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

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Use when placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.

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Use when anticipating how AEJ: Economic Policy referees and editors will read a manuscript, before submission or before an R&R, to pre-empt the objections that sink policy-evaluation papers. Maps likely pushback to fixes and calibrates expectations; it does not write the response letter (see aejpol-rebuttal) or fix the identification.

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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.

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Use when planning an ACM CCS project timeline across the two HotCRP review cycles, from venue fit through abstract registration, full-paper upload, rebuttal, notification, revision, artifact evaluation, ACM camera-ready, and in-person presentation, with backward-planning offsets and the security big-four deadline-hopping calendar.

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Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

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Use when designing or auditing ACM CCS experiments, attack demonstrations, adaptive-attack defense evaluations, security measurements, baselines, overhead and cost reporting, ablations, and claim-to-evidence fit, with emphasis on evidence that survives an adversarial program committee rather than leaderboard wins.

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Use when revising an ACM CCS paper for a precise threat model, calibrated attack/defense claims, 12-page ACM sigconf compression, double-blind wording, adaptive-evaluation honesty, and the security house style that survives an adversarial SIGSAC program committee.

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Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.

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Use when preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.

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Use when you need to understand how Accounting, Organizations and Society (AOS) evaluates a manuscript — triage by the joint Editors-in-Chief, double-anonymized review by interdisciplinary referees, the meaning of each decision type, and how to read the editor's letter. Explains the process; it does not draft the revision response (aos-rebuttal).

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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.

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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 working with the Associate Editor and Editor of an Academy of Management Annals (Annals) review — scope alignment after a proposal is invited, what review-article referees evaluate, and revision expectations. Plans the interaction; it does not run the portal preflight (amann-submission) or draft the response letter (amann-revision).

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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.

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WCAG 2.2 Accessibility Audit skill that systematically evaluates web pages against 8 core Success Criteria (1.1.1, 1.4.3, 1.4.11, 2.1.1, 2.1.2, 2.4.3, 2.4.7, 4.1.2) using accessibility tree inspection and visual analysis. Use this skill when you need to perform accessibility testing/auditing on a live webpage.

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Minimalist UX/Interaction Audit Expert that deconstructs complex interactions through cognitive load and operational efficiency lenses. Use this skill when you need to perform a UX walkthrough audit on a Figma prototype or web interface, evaluating usability based on principles like fewer clicks, less UI elements, no hidden logic, and self-explanatory design.

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Evaluate whether a creator, influencer, or brand account reaches the right audience using public profile, aggregate demographic, geography, language, content, comment, and commerce signals. Use for creator comparisons, sponsorship fit, market fit, and audience-quality research.

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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.

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Research a company's ideal customer profiles and build detailed synthetic buyer personas. Identifies 4-6 distinct buyer segments through web research, then creates rich, realistic personas with demographics, motivations, skepticism profiles, decision criteria, and language patterns. Use when you need to understand who your buyers are at a deep level — their motivations, objections, and how they evaluate solutions.

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End-to-end leadership change signal composite. Takes any set of companies, detects recent leadership changes (new VP+, C-suite hires and promotions), evaluates relevance to your product, and drafts personalized outreach. Uses Apollo People Search (free) for fast detection + Apollo Enrichment (1 credit/person) for employment history, start dates, LinkedIn URLs, and verified emails.

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。