{"id":"W2931443126","doi":"10.1080/10447318.2019.1597575","title":"Runners’ Perspectives on ‘Smart’ Wearable Technology and Its Use for Preventing Injury","year":2019,"lang":"en","type":"article","venue":"International Journal of Human-Computer Interaction","topic":"Physical Activity and Health","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Wearable computer; Recreation; Activity tracker; Wearable technology; Human–computer interaction; Computer science; BitTorrent tracker; Embedded system; Artificial intelligence; Eye tracking","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001531834,0.00009576827,0.0002390084,0.0005264596,0.00005035047,0.00006685818,0.0001029359,0.00006168727,0.00004880992],"category_scores_gemma":[0.00006586219,0.00008278195,0.0001167034,0.00006259403,0.0000186454,0.0007066026,0.00003961534,0.0003738141,0.00001808339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844603,"about_ca_system_score_gemma":0.00003767869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000802609,"about_ca_topic_score_gemma":0.000001571127,"domain_scores_codex":[0.9991771,0.00002278834,0.0002879683,0.000161806,0.0002340142,0.0001163292],"domain_scores_gemma":[0.998854,0.0001426388,0.0003201175,0.00006954213,0.0005597527,0.00005398454],"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":[0.01424692,0.01040154,0.03478469,0.0006110292,0.003872438,0.0001899779,0.00944335,0.0008319718,0.6135395,0.1019075,0.006171498,0.2039996],"study_design_scores_gemma":[0.03997111,0.07899711,0.2442722,0.02406025,0.000933068,0.008083655,0.009093559,0.1050292,0.2936855,0.03709713,0.1563891,0.002388076],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948418,0.00004625796,0.0009396106,0.00269162,0.001066109,0.0002003756,0.000003494457,0.0000172628,0.0001934489],"genre_scores_gemma":[0.9973988,0.0000608823,0.0009648506,0.0002441561,0.0008826702,0.000003473873,0.000002228356,0.00001172036,0.0004312437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3198539,"threshold_uncertainty_score":0.337575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04058864782946685,"score_gpt":0.3764442565028233,"score_spread":0.3358556086733564,"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."}}