{"id":"W4408443825","doi":"10.54808/imcic2025.01.112","title":"Quantitative Endosurgery Process Analysis by Machine Learning Method","year":2025,"lang":"en","type":"article","venue":"Proceedings","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Machine learning; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001802345,0.0008712328,0.0006981296,0.003107315,0.0004367041,0.001253669,0.0008357235,0.0008126837,0.002352177],"category_scores_gemma":[0.00369068,0.0002900486,0.001132083,0.001521548,0.0006420654,0.00107316,0.0006393962,0.0009737089,0.0007021149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008221501,"about_ca_system_score_gemma":0.001005384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065094,"about_ca_topic_score_gemma":0.001047685,"domain_scores_codex":[0.9987847,0.0002197759,0.00009412682,0.0003619085,0.0004225405,0.0001168794],"domain_scores_gemma":[0.9982055,0.0009104018,0.0002644079,0.0001839668,0.0003951885,0.00004052326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002166246,0.0003152552,0.0082816,0.0003130306,0.000128771,0.0001624829,0.0002307527,0.4766811,0.02355461,0.01539026,0.001845096,0.4728805],"study_design_scores_gemma":[0.000002570024,0.00002710853,0.0009521959,0.000005522759,0.000007051715,0.00002734122,0.00001869263,0.9930275,0.002716132,0.002729341,0.000476572,0.000009997029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01005033,0.00007828658,0.9885035,0.00004350586,0.00001887509,0.00005155326,0.00008048846,0.0006826813,0.0004907727],"genre_scores_gemma":[0.5380072,0.0002203095,0.4588998,0.00004929032,0.00007044392,0.0004201868,0.0004818671,0.0001473834,0.001703485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003107315,"threshold_uncertainty_score":0.009531796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007319970449672697,"score_gpt":0.2903313112537253,"score_spread":0.2830113408040526,"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."}}