{"id":"W3158090006","doi":"10.5539/hes.v11n2p201","title":"Sensorization of Things Intelligent Technology for Sport Science to Develop an Athlete’s Physical Potential","year":2021,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Mongkut's University of Technology North Bangkok","keywords":"Excellence; Athletes; Sports science; Competition (biology); Reputation; Perception; Amateur; Competitive athletes; Process (computing); Computer science; Engineering; Applied psychology; Psychology; Physical therapy; Medicine; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006681115,0.0000837682,0.0001601723,0.0001135475,0.00008197866,0.00001409103,0.00007337777,0.00002467307,0.000005300712],"category_scores_gemma":[0.00007315804,0.00008301976,0.00001324479,0.0006249317,0.00007304858,0.0001483007,0.00003516005,0.00002515332,0.000005268355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006316437,"about_ca_system_score_gemma":0.0001208286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002034344,"about_ca_topic_score_gemma":6.553779e-7,"domain_scores_codex":[0.9994309,0.000004053024,0.0001616296,0.0001734324,0.00009495849,0.000135055],"domain_scores_gemma":[0.998765,0.00001526151,0.00003289489,0.0001352111,0.001016548,0.00003508367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006062714,0.00008417588,0.0001664698,0.0001002317,0.00003247316,6.246732e-7,0.001403685,0.01864421,0.9269071,0.04789328,0.0002072339,0.004554414],"study_design_scores_gemma":[0.00006405553,0.0000288743,0.002438407,0.00004775941,0.00002116746,0.000003830296,0.002195461,0.0001916911,0.9797861,0.002918317,0.01214965,0.0001547052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870784,0.0001986426,0.01035641,0.000209896,0.001745576,0.00007605857,0.000002051122,0.0001293536,0.0002036541],"genre_scores_gemma":[0.9642209,0.00003566291,0.03477692,0.00005644003,0.0002034025,0.00004694839,0.00001034337,0.00001448327,0.0006349023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05287894,"threshold_uncertainty_score":0.3385448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025879022122275,"score_gpt":0.3277271526253871,"score_spread":0.2974683624041644,"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."}}