{"id":"W4411334552","doi":"10.54808/jsci.23.03.1","title":"Quantitative Endosurgery Process Analysis by Machine Learning Method","year":2025,"lang":"en","type":"article","venue":"Journal of systemics, cybernetics, and informatics/Journal of systemics cybernetics and informatics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Process (computing); Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004765566,0.0006127774,0.002457043,0.001899195,0.0001881803,0.0005297923,0.0006242049,0.0003698988,0.00001474527],"category_scores_gemma":[0.0008147242,0.0004948236,0.0005676228,0.001144249,0.0002460763,0.001128158,0.0001192213,0.001489033,0.000003620456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067932,"about_ca_system_score_gemma":0.0002043243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002958891,"about_ca_topic_score_gemma":0.000003875979,"domain_scores_codex":[0.9909774,0.0001729068,0.006998554,0.0001193884,0.001160535,0.0005712266],"domain_scores_gemma":[0.9919237,0.0008310431,0.004507965,0.0003116457,0.00185892,0.0005666694],"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":[0.0004198292,0.0005309755,0.1135632,0.05055694,0.03970682,0.0001315549,0.1674847,0.5493969,0.001322433,0.007470315,0.03154795,0.03786841],"study_design_scores_gemma":[0.001836495,0.0003802738,0.0001376937,0.003990704,0.003680373,0.001089275,0.03388307,0.9417096,0.0004578684,0.0002829224,0.01191191,0.0006397895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4541569,0.03919111,0.4974672,0.0002891618,0.001965294,0.0004958992,0.0001438169,0.00008633549,0.006204263],"genre_scores_gemma":[0.9454856,0.02675843,0.02675693,0.0002593084,0.0002061534,0.000003920949,0.0000345127,0.00006088963,0.0004342832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4913287,"threshold_uncertainty_score":0.9997503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005835838444036644,"score_gpt":0.2427475166767384,"score_spread":0.2369116782327018,"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."}}