{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001779968,0.0001828856,0.0001866041,0.00017704,0.00002810721,0.0000780824,0.0002221905,0.0001319383,0.0003104289],"category_scores_gemma":[0.0000279713,0.0001857834,0.00006774387,0.0001132108,0.000008299497,0.00002839762,0.0001690236,0.0003787001,0.001019357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005351008,"about_ca_system_score_gemma":0.00001280116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001453199,"about_ca_topic_score_gemma":0.0000155801,"domain_scores_codex":[0.9991713,0.00001428284,0.0002368024,0.0002514377,0.0001493565,0.00017682],"domain_scores_gemma":[0.9996427,0.00001801365,0.0000159747,0.0002079546,0.00001816967,0.00009719432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001592306,0.00001516042,0.00000238924,0.0001415704,0.00003755989,0.000001675269,0.004573146,0.9240848,0.0001154974,0.02369982,0.004339687,0.04298713],"study_design_scores_gemma":[0.00003969688,0.00001684128,0.0002071313,0.00008165558,0.000007282492,0.000001843697,0.0007732977,0.9925402,0.0001275136,0.001725523,0.004147878,0.0003311521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001195504,0.00001806352,0.8988051,0.000115478,0.0004886673,0.0002104147,0.00001931082,0.001494018,0.09765343],"genre_scores_gemma":[0.9768575,0.00004093914,0.01610596,0.0001809564,0.0002156515,0.00008505507,0.0001830588,0.00007684583,0.006254006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9756621,"threshold_uncertainty_score":0.9997585,"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."}}