{"id":"W3141187141","doi":"10.1111/joa.13435","title":"Automated analysis of rabbit knee calcified cartilage morphology using micro‐computed tomography and deep learning","year":2021,"lang":"en","type":"article","venue":"Journal of Anatomy","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"FP7 Ideas: European Research Council; H2020 Marie Skłodowska-Curie Actions; Suomen Kulttuurirahasto; Canadian Institutes of Health Research; Killam Trusts; Pohjois-Pohjanmaan Rahasto; Academy of Finland","keywords":"Cartilage; Computer science; Computed tomography; Tomography; Morphology (biology); Rabbit (cipher); Knee cartilage; Anatomy; Artificial intelligence; Biomedical engineering; Medicine; Osteoarthritis; Articular cartilage; Radiology; Pathology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006962559,0.0004052084,0.000333193,0.0009411034,0.0001654444,0.0005136421,0.0003839294,0.0005286298,0.0004662108],"category_scores_gemma":[0.0008916198,0.0004456424,0.0004196665,0.0003830185,0.000278801,0.0003316702,0.0003218376,0.0003048812,0.00021417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005034222,"about_ca_system_score_gemma":0.000589602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004530627,"about_ca_topic_score_gemma":0.008157534,"domain_scores_codex":[0.9997562,0.00002818186,0.00002019284,0.00007229859,0.00008941454,0.00003388211],"domain_scores_gemma":[0.9995585,0.00009118379,0.0001130524,0.00007019691,0.0001347729,0.0000322881],"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":[0.0003775745,0.00005961967,0.009781604,0.0001829109,0.00008717636,0.0001619741,0.0001544982,0.0203361,0.9203377,0.0004224575,0.0002630674,0.04783539],"study_design_scores_gemma":[0.0000293465,0.0004660623,0.05152993,0.00005729078,0.0001458294,0.0007710637,0.0001179984,0.4687106,0.4756454,0.0006374031,0.001800678,0.00008835421],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8521274,0.001121882,0.144043,0.00008911202,0.00002227669,0.00008060157,0.0006997923,0.0008818246,0.0009340476],"genre_scores_gemma":[0.8866153,0.0006391,0.1104119,0.00006478454,0.000009571262,0.0001153534,0.0008085436,0.0001125021,0.001222869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004530627,"threshold_uncertainty_score":0.009008527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162545919054905,"score_gpt":0.2777641238000939,"score_spread":0.2661386646095448,"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."}}