{"id":"W3170247641","doi":"10.34133/2021/9796431","title":"Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China","year":2021,"lang":"en","type":"article","venue":"Health Data Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"National Natural Science Foundation of China","keywords":"Outbreak; Mainland China; China; Population; Geography; Demography; Socioeconomics; Coronavirus disease 2019 (COVID-19); Geographic mobility; Mainland; Medicine; Disease; Infectious disease (medical specialty); Virology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008303496,0.0005760185,0.0004459571,0.003121124,0.0004253679,0.0005517951,0.000722428,0.0005421999,0.001312036],"category_scores_gemma":[0.001865652,0.0001934993,0.0004774787,0.003225144,0.0002328515,0.0005044152,0.0008684703,0.0004393418,0.0007216143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183001,"about_ca_system_score_gemma":0.001981511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1226822,"about_ca_topic_score_gemma":0.1098322,"domain_scores_codex":[0.9994714,0.00006501488,0.00007779645,0.0001587556,0.0001282418,0.00009892166],"domain_scores_gemma":[0.9987783,0.000149086,0.0002781348,0.0001830748,0.0004345948,0.0001768621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002143601,0.0001433431,0.9484565,0.0002659606,0.0001601788,0.0004206777,0.0004948498,0.005851939,0.001318496,0.0004457492,0.02242447,0.01980344],"study_design_scores_gemma":[0.00002566097,0.00005619707,0.9804919,0.00004542392,0.00003731136,0.00006700007,0.0003645777,0.01193071,0.0003454833,0.0001178257,0.00648983,0.00002808623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8074113,0.0002305142,0.000914195,0.0002713765,0.00004045849,0.0001982137,0.1888772,0.0002354062,0.001821346],"genre_scores_gemma":[0.6453762,0.000254298,0.002862908,0.00009874936,0.0000560249,0.0004940818,0.3495924,0.00002658259,0.001238763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1226822,"threshold_uncertainty_score":0.2439362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5282396810307556,"score_gpt":0.5390382230607975,"score_spread":0.01079854203004194,"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."}}