{"id":"W3112020795","doi":"10.2196/25137","title":"Wearables in the SARS-CoV-2 Pandemic: What Are They Good for?","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Wearable technology; Product (mathematics); Process (computing); Consumer privacy; Incentive; Internet privacy; Computer science; Pandemic; Quality (philosophy); Business; Risk analysis (engineering); Marketing; Medicine; Coronavirus disease 2019 (COVID-19); Information privacy","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":[0.0003741856,0.0001848003,0.0002676327,0.00005616674,0.0001362897,0.00009777555,0.0001886746,0.0001004907,9.168057e-7],"category_scores_gemma":[0.0000346438,0.0001432419,0.00004028645,0.0001964314,0.00002240692,0.0002866155,0.00002383957,0.0003487239,0.000009448105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008842985,"about_ca_system_score_gemma":0.00006241127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008200591,"about_ca_topic_score_gemma":0.0002858156,"domain_scores_codex":[0.9986727,0.0000693718,0.0003025813,0.0002452787,0.0001577843,0.0005522998],"domain_scores_gemma":[0.9993703,0.000209853,0.00006349671,0.0001692547,0.00002066235,0.0001664254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007827568,0.0003798235,0.5347347,0.03462903,0.0001330764,0.0001254299,0.08840495,0.001463727,0.0552009,0.006302113,0.02831266,0.2495309],"study_design_scores_gemma":[0.02405255,0.005589183,0.4732246,0.006461781,0.0003704805,0.0003704382,0.1100283,0.02278189,0.02993042,0.03093988,0.2898932,0.006357322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870036,0.006532301,0.0004853471,0.004037952,0.0003876517,0.0009419885,0.00002214098,0.000206543,0.0003824847],"genre_scores_gemma":[0.9917871,0.004397866,0.0002393296,0.002869121,0.0005032461,0.0001565276,0.000005909628,0.000038054,0.000002817051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2615805,"threshold_uncertainty_score":0.5841236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09845213473357906,"score_gpt":0.3484713062891782,"score_spread":0.2500191715555992,"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."}}