{"id":"W4220654755","doi":"10.2196/36238","title":"Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing","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":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Computer science; Tracing; Pipeline (software); Sentiment analysis; Set (abstract data type); Domain (mathematical analysis); Artificial intelligence; Data science; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002306017,0.0004344712,0.0003915462,0.002714652,0.0004301682,0.001679161,0.000448586,0.0006049469,0.001050524],"category_scores_gemma":[0.009944049,0.0001848292,0.0005375579,0.001302407,0.0005258797,0.001540827,0.0006997018,0.000727714,0.0005493736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001652,"about_ca_system_score_gemma":0.0004163718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004785426,"about_ca_topic_score_gemma":0.006577263,"domain_scores_codex":[0.9982315,0.0006830135,0.0001496232,0.0003519764,0.0004820242,0.0001018739],"domain_scores_gemma":[0.9858397,0.009073111,0.001671657,0.0004690783,0.002569119,0.0003773498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001395938,0.001084078,0.6080985,0.002538908,0.0002815014,0.001561252,0.03048442,0.01231261,0.06317559,0.003462944,0.01031378,0.2652905],"study_design_scores_gemma":[0.00003353856,0.0006031414,0.6595824,0.0002125467,0.0001394247,0.0006731437,0.01677865,0.2921755,0.01430452,0.004933137,0.0103541,0.0002097776],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9572203,0.0002651902,0.03246209,0.0006990871,0.00003245813,0.0004988841,0.003025085,0.0005882857,0.005208635],"genre_scores_gemma":[0.9817002,0.0001002778,0.01528856,0.00009357661,0.00002307979,0.0002328423,0.001873079,0.00004386103,0.0006444774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004785426,"threshold_uncertainty_score":0.01219553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.174299077383641,"score_gpt":0.4584629605705705,"score_spread":0.2841638831869294,"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."}}