{"id":"W4308552780","doi":"10.2196/39570","title":"Using a Proximity-Detection Technology to Nudge for Physical Distancing in a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Wearable technology; Context (archaeology); Distancing; Computer science; Coronavirus disease 2019 (COVID-19); Data science; Human–computer interaction; Internet privacy; Infectious disease (medical specialty); Medicine","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003253469,0.0001935253,0.0002869131,0.001060381,0.002230996,0.0003473308,0.001070322,0.00004246977,0.000002235446],"category_scores_gemma":[0.001006251,0.0001674407,0.00008244308,0.005059645,0.0001048968,0.001262004,0.002392173,0.001410725,0.000003277455],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006664363,"about_ca_system_score_gemma":0.0004565343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004899946,"about_ca_topic_score_gemma":0.002300741,"domain_scores_codex":[0.996501,0.0006596248,0.0003480728,0.0006795872,0.0009637541,0.0008479852],"domain_scores_gemma":[0.9976419,0.001170073,0.0001130534,0.0006599422,0.0002426797,0.00017233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002413966,0.004095168,0.1452482,0.0009052401,0.0002042828,0.004632976,0.7840019,0.01137387,0.01433579,0.01112464,0.0001770073,0.0214869],"study_design_scores_gemma":[0.01085316,0.01220047,0.03397421,0.0001644823,0.00002490704,0.007369887,0.3942454,0.5046354,0.00342117,0.02868842,0.00255703,0.001865435],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8881554,0.00001178112,0.1062715,0.001252793,0.00008740823,0.003972947,0.00001979928,0.000190482,0.00003790669],"genre_scores_gemma":[0.9958792,2.585044e-7,0.0002179387,0.0001524858,0.00004592807,0.003645794,6.098576e-7,0.00002056733,0.00003727007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4932615,"threshold_uncertainty_score":0.999068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1122398956403116,"score_gpt":0.4565361941767647,"score_spread":0.3442962985364531,"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."}}