{"id":"W3048607454","doi":"10.1002/ajhb.23468","title":"Using point clouds to investigate the relationship between trabecular bone phenotype and behavior: An example utilizing the human calcaneus","year":2020,"lang":"en","type":"article","venue":"American Journal of Human Biology","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"H2020 European Research Council; Division of Behavioral and Cognitive Sciences; Biotechnology and Biological Sciences Research Council; Research Councils UK; Pennsylvania State University; University of Pennsylvania; National Science Foundation","keywords":"Calcaneus; Anisotropy; Trabecular bone; Volume fraction; Degree (music); Univariate; Volume (thermodynamics); Cancellous bone; Point (geometry); Anatomy; Mathematics; Statistics; Geology; Biology; Materials science; Geometry; Physics; Medicine; Multivariate statistics; Osteoporosis; Pathology; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009869342,0.0001311757,0.0004502111,0.00009708654,0.0005194325,0.00002575489,0.0002001359,0.0000545493,0.00002488118],"category_scores_gemma":[0.0002785175,0.00007577778,0.00007257656,0.0003049331,0.0008630307,0.00005190794,0.00007323524,0.0007502449,0.000002709247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000529426,"about_ca_system_score_gemma":0.0001811726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876949,"about_ca_topic_score_gemma":0.0001031723,"domain_scores_codex":[0.9981539,0.0005678975,0.0005415727,0.0001997141,0.0001917757,0.0003451016],"domain_scores_gemma":[0.9983776,0.0002988586,0.0003160178,0.0002429123,0.000129318,0.0006352892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001180376,0.00004933614,0.9182093,0.00002579285,0.00004553237,0.00006137475,0.005647674,0.000002962961,0.06744449,0.001777196,0.0001341764,0.00648419],"study_design_scores_gemma":[0.0007007931,0.006090289,0.9863366,0.00004233058,0.0002134478,0.0002918035,0.004303033,0.00001579512,0.0003074354,0.0006191967,0.0009645915,0.0001146455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894625,0.0003422248,0.00006296604,0.009750208,0.00002861007,0.0002860099,0.000004639967,0.00001073615,0.000052132],"genre_scores_gemma":[0.9945939,0.00001046784,0.0006626317,0.004252824,0.0004401808,0.000004802745,0.000008965377,0.0000212883,0.000004906877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0681274,"threshold_uncertainty_score":0.3995108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2894464364290088,"score_gpt":0.4303742437547177,"score_spread":0.1409278073257089,"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."}}