{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001918649,0.0001259285,0.0007083353,0.000780229,0.00005526169,0.00001632594,0.0000378811,0.0001064753,0.00009303381],"category_scores_gemma":[0.00004468805,0.0001105796,0.0003406534,0.001071482,0.0000535108,0.0000749231,0.00003126246,0.0002046638,6.291985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003409807,"about_ca_system_score_gemma":0.00008629646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009968118,"about_ca_topic_score_gemma":0.0000140457,"domain_scores_codex":[0.9988937,0.0001005438,0.0004775458,0.0001440496,0.0002035659,0.0001805672],"domain_scores_gemma":[0.9989172,0.00005912631,0.000367136,0.0001167367,0.0003900033,0.0001498064],"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":[0.0002130476,0.0001916659,0.07308616,0.00004782308,0.001949189,0.003459343,0.0004799229,0.0002762442,0.9143293,0.00007170266,0.00002378339,0.005871824],"study_design_scores_gemma":[0.01509934,0.003782183,0.09174717,0.0005690898,0.01816367,0.006950934,0.002450777,0.02138899,0.8380216,0.00008758765,0.001306779,0.0004318221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937274,0.005428026,0.0004466026,0.00008678941,0.0001102794,0.00006546675,0.00000313679,0.0000281299,0.000104148],"genre_scores_gemma":[0.9913945,0.00009975951,0.008309968,0.00007419116,0.00004231833,4.264891e-7,0.00001713336,0.00001320867,0.00004855108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07630763,"threshold_uncertainty_score":0.4509305,"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."}}