{"id":"W193323899","doi":"10.1007/978-3-642-35758-9_42","title":"A BREP Model and Mesh Errors Detecting Tool: TopoVisu","year":2012,"lang":"en","type":"book-chapter","venue":"IFIP International Federation for Information Processing/IFIP","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Software portability; Computer science; Documentation; Polygon mesh; Visualization; Feature (linguistics); Software engineering; Human–computer interaction; Data mining; Programming language; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003083647,0.0004677357,0.0003110534,0.0004235758,0.0004475928,0.001104002,0.0002154329,0.0004379125,0.0001564758],"category_scores_gemma":[0.00009340343,0.000503978,0.00011473,0.00003935682,0.00004181268,0.003647716,0.00005813717,0.0003305468,0.00005108891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002343027,"about_ca_system_score_gemma":0.00009691596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003139346,"about_ca_topic_score_gemma":0.00001204926,"domain_scores_codex":[0.997951,0.000004561473,0.000982132,0.0002560968,0.0005135356,0.000292683],"domain_scores_gemma":[0.9985467,0.00004972426,0.0004940555,0.0001648142,0.0006404158,0.0001043284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001304736,0.00002502823,0.00001857615,0.002546119,0.0002434198,6.532748e-7,0.00349121,0.4390285,0.0000974763,0.1049554,0.003974338,0.4454888],"study_design_scores_gemma":[0.0005622598,0.00002577469,0.00002168854,0.0002651602,0.00006361843,0.00002260873,0.00005529282,0.8371001,0.0007710627,0.006649103,0.1537874,0.0006759086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007355675,0.0005936167,0.5945539,0.0003235773,0.001493912,0.001177516,0.0002823349,0.00100158,0.399838],"genre_scores_gemma":[0.8755112,0.000533238,0.02660153,0.000934436,0.001164459,0.0004731763,0.004097172,0.0002788715,0.09040589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8747756,"threshold_uncertainty_score":0.9999329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727343074200252,"score_gpt":0.2298107007570109,"score_spread":0.2125372700150084,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}