{"id":"W4409814780","doi":"10.1016/j.procs.2025.03.054","title":"Developing Skeletal Activity Scheduler using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Physical Activity and Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Computer science; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560671,0.0001113861,0.0002303705,0.000187052,0.0004342612,0.00006257563,0.0001773479,0.00003637296,0.000003881831],"category_scores_gemma":[0.0001204405,0.00009718005,0.00003979493,0.001131212,0.0002372586,0.0004226254,0.0002630382,0.0003725872,0.00001194547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002217914,"about_ca_system_score_gemma":0.001395596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004879412,"about_ca_topic_score_gemma":0.000004702476,"domain_scores_codex":[0.9987981,0.00002003546,0.0001155392,0.0003925174,0.0003067786,0.0003670052],"domain_scores_gemma":[0.9994708,0.00006407374,0.00005294777,0.0001393703,0.0001431541,0.0001296196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001912475,0.001435724,0.1093268,0.001935449,0.00007906707,0.00004107267,0.001289394,0.000912485,0.2547686,0.04277013,0.00004761619,0.5872024],"study_design_scores_gemma":[0.0005513161,0.0001764711,0.06755529,0.0003334814,0.0000200729,0.00003567161,0.000005797734,0.887173,0.04167824,0.001283403,0.0009981231,0.0001891322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6710947,0.00002826102,0.3269142,0.001224297,0.0001995471,0.0001231622,2.494867e-7,0.00007576044,0.0003397833],"genre_scores_gemma":[0.9199099,0.000007001519,0.07919163,0.0006826751,0.0001550388,0.000003294452,5.704567e-7,0.000004710688,0.0000452116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8862605,"threshold_uncertainty_score":0.3962888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05792746949389891,"score_gpt":0.3589306331328357,"score_spread":0.3010031636389368,"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."}}