{"id":"W3007244130","doi":"10.1080/17458927.2020.1722421","title":"Training by feel: wearable fitness-trackers, endurance athletes, and the sensing of data","year":2020,"lang":"en","type":"article","venue":"The Senses and Society","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"BitTorrent tracker; Activity tracker; Wearable computer; Athletes; Amateur; Embodied cognition; Applied psychology; Computer science; Wearable technology; Tracking (education); Psychology; Human–computer interaction; Artificial intelligence; Eye tracking; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0004199948,0.0000821191,0.0001407318,0.000004873951,0.0003001737,0.00006845731,0.0003791738,0.00004783349,0.000001635429],"category_scores_gemma":[0.00005918828,0.00004900522,0.00002935942,0.0001741369,0.0005450469,0.0002975575,0.0003386319,0.0002430602,8.860189e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005566889,"about_ca_system_score_gemma":0.00001590302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003818085,"about_ca_topic_score_gemma":0.00000214807,"domain_scores_codex":[0.9993258,0.00006838967,0.0001372648,0.0002340518,0.000102338,0.0001321289],"domain_scores_gemma":[0.9992067,0.0002555759,0.0001100611,0.0003642594,0.00004785214,0.00001550886],"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":[0.00009883305,0.00003517907,0.0001334335,0.0001491553,0.000454073,0.00001001522,0.2502903,0.00003142475,0.1399482,0.117273,0.04724531,0.444331],"study_design_scores_gemma":[0.002662185,0.0001421566,0.001299296,0.0002328981,0.00008305749,0.0002170544,0.04131854,0.8881761,0.01909987,0.009081328,0.03716536,0.0005221446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7926614,0.004320818,0.1350434,0.06617671,0.0002404203,0.0004102014,0.00005100016,0.0002238178,0.0008722201],"genre_scores_gemma":[0.9902147,0.0005084446,0.00702489,0.002150452,0.0000438193,5.710211e-7,0.000002885471,0.000005198667,0.00004901312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8881447,"threshold_uncertainty_score":0.2308724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07026750212102262,"score_gpt":0.2840679314222855,"score_spread":0.2138004293012629,"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."}}