{"id":"W2921964242","doi":"10.1016/j.heliyon.2019.e01241","title":"Using technology to enhance and encourage dance-based exercise","year":2019,"lang":"en","type":"article","venue":"Heliyon","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dance; Context (archaeology); Psychology; Dance education; Applied psychology; Sociology; Multimedia; Computer science; Visual arts; Art","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":[0.0009212585,0.0003780289,0.0001939622,0.00111058,0.0006772462,0.001525083,0.0005452343,0.0007673088,0.006303382],"category_scores_gemma":[0.003555319,0.000174679,0.0005771957,0.0004774324,0.0008145769,0.001332542,0.002285354,0.0004021487,0.001288181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522098,"about_ca_system_score_gemma":0.0003855441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004458264,"about_ca_topic_score_gemma":0.001156389,"domain_scores_codex":[0.9991736,0.0003885942,0.00005966667,0.0001085495,0.0001720183,0.00009759326],"domain_scores_gemma":[0.9990282,0.0006061576,0.00009970901,0.00009992948,0.00006747273,0.00009855384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006381249,0.001869329,0.04448948,0.003486441,0.0001796106,0.002024726,0.06120483,0.001882961,0.07291815,0.01288516,0.00733483,0.7910864],"study_design_scores_gemma":[0.0006060168,0.009482034,0.2274548,0.003458431,0.0005523189,0.00690433,0.08735221,0.01543677,0.04147643,0.02518385,0.5816475,0.0004453491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8750529,0.001560692,0.04804948,0.001726613,0.0002810276,0.0005527156,0.0001549007,0.0008433707,0.07177821],"genre_scores_gemma":[0.9274383,0.001247417,0.05278926,0.0003820188,0.00008379192,0.0004581485,0.000131374,0.00009628585,0.01737344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006303382,"threshold_uncertainty_score":0.02108687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535816317214461,"score_gpt":0.3270304642086081,"score_spread":0.3116723010364635,"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."}}