950 件 ・ 関連度順
概要と使いどころ
Perform the final submission-level audit of mathematical-modeling workflow integrity, evidence quality, anti-fabrication, paper coherence, figures, references, and contest readiness after consistency and completeness audits pass.
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。
Draft submission-ready mathematical-modeling paper sections from the approved solution package, frozen numbers, human decision ledger, and verified figures without searching scattered exploratory outputs or inventing interpretation.
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。
Classify each parsed mathematical-modeling subquestion by required output and structure, surface ambiguous framing trade-offs for human choice, and record primary/secondary task types without selecting algorithms.
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。
Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.
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。
Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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。
Manage and verify references for mathematical modeling contest papers, generating BibTeX entries, checking citation completeness, and ensuring all references are traceable to actual sources.
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。
Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and 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。
Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.
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。
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.
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。
Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, and experiment outputs, saving one compact JSON review.
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。
Package a G6-approved mathematical-modeling paper and its exact supporting code, data, results, and figures without changing their behavior or the workspace.
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。
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
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。
Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.
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。
Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
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 modeling in Autodesk Maya, such as hard-surface props, weapons, vehicles, a sci-fi crate, a game-ready or SubD film model, or a character or creature base mesh, or when asked to "clean up this model" or a scene before rigging or UVs. Also for support loops vs bevels vs creases, Smart Bevel, weighted normals, hard edges, pinching or wobbly highlights, a faceted cage, flipped faces, a triangle budget, n-gons, non-manifold geometry, mirroring a half, a low poly from a high poly, or deformation-ready topology.
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。
STRIDE threat modeling, DREAD risk scoring, secret detection, and secure architecture design. Use when conducting threat models, reviewing code for vulnerabilities, designing defense-in-depth, or scanning for hardcoded secrets.
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。
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
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。
Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
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。
Domain modeling with algebraic data types, smart constructors, and type-level error handling
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。
Event Modeling facilitation technique — brainstorm events, identify commands and views, define aggregate boundaries, write Given-When-Then specifications
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。
Check a mathematical-modeling paper's in-text citations, bibliography, appendix length and content, code, supporting files, reproducibility, consistency, and anonymity. Use when the user provides a paper or related materials and wants one consolidated P0-P3 audit; do not generate or invent missing sources or programs.
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。
Review the symbol-notation section of a mathematical-modeling paper against the full paper. Check table coverage, symbol meanings, units, overloading, case and subscript consistency, first-use definitions, formula usage, and visible table formatting. Use for symbol-table diagnosis, not general paper review.
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。
Review the model establishment, numerical solution, result analysis, model validation, and sensitivity or robustness sections of a mathematical-modeling paper against the complete problem. Use for core-model-section diagnosis, not automatic full-paper scoring or unsupported recomputation.
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。