{"id":"W4398147089","doi":"10.1016/j.bone.2024.117131","title":"Characterization and quantification of in-vitro equine bone resorption in 3D using μCT and deep learning-aided feature segmentation","year":2024,"lang":"en","type":"article","venue":"Bone","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Resorption; Bone resorption; Osteoclast; Chemistry; Biomedical engineering; Pathology; Anatomy; Medicine; In vitro; Internal medicine; Biochemistry","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":[0.0004984611,0.0003557987,0.0003747133,0.0007859771,0.0001729097,0.0007264037,0.0003863469,0.0006177191,0.001076326],"category_scores_gemma":[0.0004681149,0.0003597944,0.0003103062,0.0005769955,0.000402286,0.0003225004,0.0002756142,0.000376497,0.0003185151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003518474,"about_ca_system_score_gemma":0.0006063848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003945004,"about_ca_topic_score_gemma":0.007120667,"domain_scores_codex":[0.9998119,0.00001734277,0.00001013934,0.00003686671,0.0001006318,0.00002317326],"domain_scores_gemma":[0.9997262,0.00007543177,0.00005376135,0.00003693417,0.00009288358,0.00001478677],"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":[0.0002484787,0.00009179669,0.009652117,0.0002263087,0.00003669013,0.0001601741,0.0001921468,0.01882265,0.9304473,0.0005367275,0.0002833697,0.03930224],"study_design_scores_gemma":[0.00001748653,0.0002945751,0.09578379,0.00007954048,0.00009580133,0.001336313,0.0002819095,0.2568339,0.6397504,0.0007795381,0.004662924,0.00008389716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8021519,0.002000806,0.1907778,0.0001264966,0.00002646641,0.0000785764,0.001484354,0.0006448022,0.0027087],"genre_scores_gemma":[0.9224318,0.0008286141,0.07330624,0.00006208543,0.00000927009,0.00009333246,0.0007108574,0.0001221158,0.00243566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003945004,"threshold_uncertainty_score":0.00784409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157181647774464,"score_gpt":0.279565931557424,"score_spread":0.2679941150796794,"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."}}