{"id":"W6922809026","doi":"10.1371/journal.pdig.0000405.s003","title":"Incidence for each virus analized, weather and mobility time series and correlations in Canada.","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Incidence (geometry); Time series; Virus","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004977133,0.0003842071,0.0003749368,0.00347675,0.001115779,0.0008695509,0.0008605484,0.0003498813,0.01823471],"category_scores_gemma":[0.003898767,0.0002953117,0.0007785051,0.008908786,0.0003154763,0.0004207835,0.0007144623,0.001100939,0.001495402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01224925,"about_ca_system_score_gemma":0.03352236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971595,"about_ca_topic_score_gemma":0.9977319,"domain_scores_codex":[0.9993622,0.00003802676,0.00004787023,0.00007319853,0.0002459268,0.0002328362],"domain_scores_gemma":[0.9974323,0.000176568,0.0003291277,0.000101823,0.001561567,0.0003985884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003069902,0.00004706329,0.5031438,0.0003566658,0.0002515116,0.00008383166,0.0006133588,0.001941413,0.0001697653,0.002470475,0.44625,0.04436513],"study_design_scores_gemma":[0.00002520244,0.00002158761,0.9559098,0.0001351492,0.00008813445,0.00006497536,0.0009760912,0.001006704,0.0001222819,0.0001984041,0.04142356,0.00002805525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1187841,0.003553118,0.0009381385,0.001872202,0.0002327825,0.0001377503,0.8407075,0.0004727962,0.03330167],"genre_scores_gemma":[0.5429289,0.005566607,0.003022658,0.0004803393,0.00008108284,0.000175701,0.3645068,0.0003003485,0.08293743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01823471,"threshold_uncertainty_score":0.08887494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2218537411254614,"score_gpt":0.3864797528873234,"score_spread":0.164626011761862,"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."}}