{"id":"W4368228064","doi":"10.2139/ssrn.4435964","title":"Who is the User? A Feature Analysis of 'Children’s' Smartwatches in China During COVID-19","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Child Development and Digital Technology","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smartwatch; Coronavirus disease 2019 (COVID-19); China; Feature (linguistics); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Computer science; Geography; Virology; Medicine; Linguistics; Wearable computer; Infectious disease (medical specialty); Outbreak; Disease","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003174175,0.0002633182,0.0006260766,0.001126822,0.0006016709,0.0001764245,0.001348908,0.0005112051,0.00004457574],"category_scores_gemma":[0.0006633477,0.0002048638,0.0005254282,0.002225769,0.0003000303,0.0001214139,0.0005518488,0.004267978,0.000007264891],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002183676,"about_ca_system_score_gemma":0.006892862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006181721,"about_ca_topic_score_gemma":0.1556997,"domain_scores_codex":[0.9963378,0.0002309486,0.0004866179,0.0004403084,0.0006280843,0.001876197],"domain_scores_gemma":[0.998756,0.0001547779,0.000508461,0.0003956402,0.00006203941,0.0001231074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008295831,0.00009011107,0.8745032,0.00002909756,0.004724187,0.00001722015,0.02712996,0.001062406,0.00001011282,0.08826178,0.001592293,0.002496667],"study_design_scores_gemma":[0.000555438,0.00003688016,0.5491353,0.00007747368,0.0005545632,0.00002464079,0.007741782,0.00003076405,0.0000373,0.4394215,0.001946415,0.0004379813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588568,0.001798571,0.0002686505,0.03755983,0.0001690703,0.00034479,0.00003015263,0.0001119626,0.0008601755],"genre_scores_gemma":[0.9787058,0.01367106,0.00002082533,0.0001771893,0.0001754908,0.00001346331,0.00002547348,0.00002438463,0.007186342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3511597,"threshold_uncertainty_score":0.9987372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457752586032673,"score_gpt":0.2922702467787153,"score_spread":0.2776927209183886,"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."}}