{"id":"W4367849166","doi":"10.32920/22734323.v1","title":"A Machine Intelligence Approach to Virtual Ballet Training","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Human Motion and Animation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Toronto Metropolitan University","funders":"","keywords":"Dance; Visualization; Ballet; Computer science; Virtual reality; Trajectory; Artificial intelligence; Movement (music); Space (punctuation); Human–computer interaction; Computer vision; Computer graphics (images); Visual arts","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":[0.00119345,0.0007086091,0.0008148021,0.00154057,0.0004607271,0.002325001,0.001599894,0.0009973937,0.00364786],"category_scores_gemma":[0.003025101,0.0003428455,0.0008559533,0.001476658,0.001381907,0.001302207,0.001211045,0.001467109,0.0008528327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009409605,"about_ca_system_score_gemma":0.0006626465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003278944,"about_ca_topic_score_gemma":0.002512203,"domain_scores_codex":[0.9990941,0.0003659807,0.00004950262,0.0002274564,0.0002086905,0.00005426563],"domain_scores_gemma":[0.9991108,0.0005647718,0.00005970322,0.0001189007,0.0001153006,0.0000305863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006950954,0.0001641378,0.00146815,0.0003590656,0.0002199952,0.0001659604,0.0002974384,0.3686521,0.002237335,0.1927592,0.007858531,0.4257487],"study_design_scores_gemma":[0.000009359061,0.00003945862,0.0005633435,0.00005472104,0.00001642065,0.00005985536,0.00008786713,0.8200827,0.0007333656,0.1682758,0.01005896,0.00001819834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003372136,0.001704281,0.985157,0.0009884799,0.00009408683,0.00005524806,0.0001144523,0.0004695884,0.008044662],"genre_scores_gemma":[0.3033354,0.00279523,0.6802212,0.0004833918,0.000470753,0.0003991781,0.0004643979,0.0001474692,0.01168299],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00364786,"threshold_uncertainty_score":0.01220334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0933431547048279,"score_gpt":0.2731511227761739,"score_spread":0.179807968071346,"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."}}