{"id":"W4394445814","doi":"10.6084/m9.figshare.19929887","title":"Pose Tracking Codes","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Computer science; Tracking (education); Artificial intelligence; Computer vision; Psychology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004863875,0.0002033123,0.0001697653,0.00002730363,0.0001327605,0.00004656333,0.0005043827,0.0002974653,0.5675061],"category_scores_gemma":[0.0006407337,0.0001774919,0.0001402,0.00006152289,0.000006083978,8.977618e-7,0.0007445074,0.0002909215,0.0003042319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001199633,"about_ca_system_score_gemma":0.00005638261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005861946,"about_ca_topic_score_gemma":0.00001087463,"domain_scores_codex":[0.9990041,0.000068717,0.0001534995,0.0004129001,0.000124372,0.0002363844],"domain_scores_gemma":[0.9993933,0.00002660021,0.0001209727,0.0003592995,0.00004239604,0.00005738081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009238349,0.00002621722,0.000001138321,0.0000728501,0.00002342305,0.00002553553,6.310062e-7,0.000005510346,0.0006831373,7.423419e-8,0.9982294,0.0009228674],"study_design_scores_gemma":[0.00007840681,0.0001739988,0.0000315,0.0001074972,0.000009040288,0.00001698438,0.000004546291,0.000001156226,0.000857981,0.000002615614,0.9984582,0.0002580511],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005367337,0.0009103314,9.912586e-8,0.00001110763,0.0001366196,0.0001159067,0.9985141,0.00001625042,0.0002419303],"genre_scores_gemma":[0.0001934512,0.00002757825,0.000009036213,0.0005726377,0.0006481832,0.00006551012,0.9982983,0.00001341166,0.0001718686],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5672018,"threshold_uncertainty_score":0.7237908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210087602819613,"score_gpt":0.2860973532802154,"score_spread":0.2439964772520193,"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."}}