{"id":"W4408599608","doi":"10.1117/12.3042275","title":"Spatial-frequency 3D fluorescence for surgical guidance: margin thickness quantification","year":2025,"lang":"en","type":"article","venue":"","topic":"Intraocular Surgery and Lenses","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Margin (machine learning); Computer science; Biomedical engineering; Materials science; Medicine; Machine learning","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":[0.000537419,0.0003847862,0.0001701903,0.000517236,0.0001142511,0.0003627552,0.0003295471,0.0004350917,0.00100335],"category_scores_gemma":[0.001370671,0.0002064209,0.0002702075,0.00023007,0.0001978768,0.0003588938,0.0003531614,0.000326916,0.00023503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005648739,"about_ca_system_score_gemma":0.0004822842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002643598,"about_ca_topic_score_gemma":0.004445483,"domain_scores_codex":[0.9998342,0.0000339786,0.000006794592,0.00003217913,0.00008018063,0.00001259732],"domain_scores_gemma":[0.9996147,0.000171509,0.00007497084,0.00004666077,0.00007395868,0.00001808759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002784706,0.00009884029,0.008430731,0.0002049,0.00005053442,0.0001027212,0.0001556851,0.3857634,0.3053155,0.002025621,0.001405497,0.2961681],"study_design_scores_gemma":[0.000007172114,0.00006291728,0.003695636,0.000008841595,0.000008949861,0.0001238086,0.0000156668,0.930868,0.06352361,0.0007459648,0.0009222779,0.00001723095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1889695,0.0004338886,0.8066327,0.000250272,0.0000221139,0.00005396312,0.0003641559,0.001689678,0.001583697],"genre_scores_gemma":[0.7161781,0.0002754638,0.2821798,0.00007077694,0.000009265887,0.00004597083,0.0002445543,0.0001328547,0.0008632187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002643598,"threshold_uncertainty_score":0.005256414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036430089568153,"score_gpt":0.3054900541015521,"score_spread":0.2851257532058706,"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."}}