{"id":"W2154161656","doi":"10.1197/jamia.m2178","title":"Finding Leading Indicators for Disease Outbreaks: Filtering, Cross-correlation, and Caveats","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Institut National de Santé Publique du Québec; McGill University Health Centre","funders":"","keywords":"Scale (ratio); Econometrics; Sample (material); Computer science; Data science; Outbreak; Statistics; Medicine; Geography; Mathematics; Cartography","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":[0.1639393,0.001878301,0.002740924,0.005107232,0.005286747,0.007206478,0.009449456,0.006277417,0.0091977],"category_scores_gemma":[0.5726829,0.00108085,0.005230655,0.006743614,0.00881747,0.01216297,0.004244355,0.00741086,0.001272644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002058496,"about_ca_system_score_gemma":0.004509178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04528779,"about_ca_topic_score_gemma":0.02848102,"domain_scores_codex":[0.9209734,0.04957109,0.01000463,0.00845932,0.009840179,0.001151449],"domain_scores_gemma":[0.3349175,0.6005778,0.01821622,0.03300583,0.01216679,0.001115868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009622468,0.0003544307,0.1655614,0.002430122,0.002401117,0.00341126,0.008195976,0.02017364,0.0007007174,0.4731935,0.09205608,0.2305596],"study_design_scores_gemma":[0.0004404105,0.000466755,0.04142798,0.002766137,0.001236615,0.004247401,0.003335868,0.1162286,0.00231272,0.7791041,0.04791127,0.0005221179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06507995,0.01058928,0.6813197,0.2023353,0.006414374,0.001434144,0.003041148,0.001849281,0.02793676],"genre_scores_gemma":[0.5670885,0.002953291,0.3696296,0.04099799,0.006761954,0.002101931,0.001187931,0.0006317253,0.008647013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1639393,"threshold_uncertainty_score":0.8670042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05529883280220668,"score_gpt":0.4032694074711659,"score_spread":0.3479705746689593,"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."}}