{"id":"W4405491508","doi":"10.1109/me61309.2024.10789720","title":"Body Performance Analysis with Machine Learning and ANOVA Methods","year":2024,"lang":"en","type":"article","venue":"","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Analysis of variance","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":[0.0005122974,0.00009092046,0.000186793,0.0003077994,0.00004995968,0.00003998681,0.00002351849,0.00003105453,0.000413054],"category_scores_gemma":[0.00001427857,0.00005919144,0.00004126041,0.000701892,0.00003506332,0.00008212844,0.00001962086,0.0002037789,0.000006566559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002702431,"about_ca_system_score_gemma":0.00002095111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002191247,"about_ca_topic_score_gemma":0.000004174127,"domain_scores_codex":[0.9993702,0.00004822113,0.0001113888,0.0001979617,0.0001702796,0.0001019028],"domain_scores_gemma":[0.9997238,0.0000415331,0.00001546491,0.0001076082,0.0000543469,0.00005724802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002022602,0.00008837925,0.6395351,0.0006467014,0.002839283,0.00008877342,0.0002412733,0.00003585132,0.2395651,0.001513135,0.000520503,0.1147236],"study_design_scores_gemma":[0.0008192714,0.001995195,0.1258534,0.0004686032,0.005487465,0.0002630619,0.00005807273,0.5602381,0.2483683,0.00006029204,0.05592808,0.0004601603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3508679,0.001970577,0.6043151,0.001374591,0.00003302622,0.0002882673,0.000001103679,0.001512542,0.03963689],"genre_scores_gemma":[0.8184645,0.000126972,0.178632,0.0001178335,0.00002357829,0.000007689829,0.00001313582,0.00001165514,0.002602685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5602022,"threshold_uncertainty_score":0.4522651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001873413872875,"score_gpt":0.3553912062292723,"score_spread":0.3353724720905435,"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."}}