{"id":"W4393393735","doi":"10.1117/12.3005886","title":"Kidney stone compositional analysis and identification using a benchtop high-resolution multi-modal x-ray phase-contrast micro-CT imaging system","year":2024,"lang":"en","type":"article","venue":"","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Phase-contrast imaging; Contrast (vision); Phase contrast microscopy; Materials science; Identification (biology); Modal; Biomedical engineering; Computer science; Radiology; Medicine; Optics; Artificial intelligence; Physics","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":[0.0006411063,0.0003877913,0.0004605203,0.0007650429,0.0002391817,0.0006551217,0.0008485136,0.0009075621,0.003090142],"category_scores_gemma":[0.0007994355,0.0003836131,0.00039848,0.0004763195,0.0002741581,0.0006420387,0.0006354274,0.0006280363,0.0008036405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002538389,"about_ca_system_score_gemma":0.0005598011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005729926,"about_ca_topic_score_gemma":0.001119354,"domain_scores_codex":[0.9995895,0.00004492493,0.00002792692,0.0001057143,0.0002055522,0.00002648198],"domain_scores_gemma":[0.9994448,0.0001534809,0.00009371075,0.00008470536,0.0001808863,0.00004239363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001440888,0.00008137653,0.002311903,0.000234756,0.00003671193,0.0001831728,0.00005580583,0.0007698483,0.9732832,0.0004043218,0.0007108813,0.02178401],"study_design_scores_gemma":[0.00005132428,0.0004026755,0.01419117,0.00006248402,0.0001269693,0.002644147,0.000106477,0.05313839,0.9199468,0.0003766439,0.008839224,0.0001136358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2772256,0.002331779,0.7080717,0.0005247872,0.0001731322,0.0005098276,0.001631182,0.005305113,0.004226805],"genre_scores_gemma":[0.3452623,0.001234999,0.6479641,0.000642554,0.0001006536,0.0003280271,0.0009134163,0.0001998404,0.003354095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003090142,"threshold_uncertainty_score":0.01033753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589495123945165,"score_gpt":0.3078324764573135,"score_spread":0.2919375252178619,"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."}}