{"id":"W2921850771","doi":"10.5430/air.v8n1p25","title":"Body sensor networks for monitoring performances in sports: A brief overview and some new thoughts","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Wearable computer; Computer science; Focus (optics); Wireless sensor network; Point (geometry); Human–computer interaction; Multimedia; Embedded system; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002670382,0.0001625961,0.0002995323,0.0003607404,0.0001857933,0.0004747964,0.0006197775,0.0001157152,0.00002999026],"category_scores_gemma":[0.0001765893,0.0001565965,0.00006750703,0.0009369108,0.00007908164,0.001395558,0.0003170574,0.0004225048,0.0001412963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009802976,"about_ca_system_score_gemma":0.0001962103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004755687,"about_ca_topic_score_gemma":0.0001234511,"domain_scores_codex":[0.9972593,0.0001911383,0.0004768193,0.00066043,0.0006743842,0.0007379305],"domain_scores_gemma":[0.9979929,0.0009637062,0.00008463527,0.0005068216,0.0002609899,0.0001909229],"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":[0.00007421822,0.00009119102,0.01226802,0.0001166418,0.00001284598,0.00001810339,0.001491666,0.000283478,0.001256514,0.01223071,0.0001123328,0.9720443],"study_design_scores_gemma":[0.0003516048,0.0008468629,0.01163289,0.001580996,0.00000882034,0.00006671504,0.00334912,0.8069592,0.06800701,0.08837107,0.0177773,0.001048461],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7405869,0.003433899,0.2500427,0.001471441,0.001683501,0.002147956,0.000003235459,0.0001411565,0.0004892176],"genre_scores_gemma":[0.9956806,0.0009371177,0.001968801,0.00003421877,0.000806652,0.00007413343,0.00000125894,0.00001631025,0.000480909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9709958,"threshold_uncertainty_score":0.6385821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2029045541022969,"score_gpt":0.4145204979329958,"score_spread":0.2116159438306989,"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."}}