{"id":"W4408906477","doi":"10.2196/preprints.75025","title":"Analyzing Public Google Search Interest on Measles in Canada: Identifying Key Moments for Targeted Risk Communication (Preprint)","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Key (lock); Internet privacy; Computer science; Data science; Computer security; World Wide Web","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":[0.0004776184,0.0003313541,0.0003186976,0.004983156,0.0009407834,0.002700449,0.0004938003,0.0003930214,0.004534797],"category_scores_gemma":[0.004919989,0.0001362098,0.0004087556,0.01107146,0.0004666534,0.0006565612,0.0009385965,0.0004645707,0.001557885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007941648,"about_ca_system_score_gemma":0.01554642,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9658386,"about_ca_topic_score_gemma":0.9771713,"domain_scores_codex":[0.9993166,0.00003257867,0.00004551763,0.00008833851,0.0002927805,0.0002241871],"domain_scores_gemma":[0.9955181,0.0007495293,0.0009655468,0.0001470364,0.002151606,0.0004682323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002103269,0.00004331364,0.8613472,0.0004037389,0.0001174912,0.0003091075,0.002468076,0.001859428,0.0008455056,0.002311682,0.1012674,0.02881674],"study_design_scores_gemma":[0.000007665356,0.00001748732,0.9623829,0.0001193463,0.00004789742,0.00009291296,0.005353206,0.004204122,0.0006438809,0.0002777292,0.02681402,0.00003893639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6726164,0.001374011,0.0006402527,0.002543685,0.0001010746,0.00009538941,0.3030906,0.0005452163,0.01899321],"genre_scores_gemma":[0.8848336,0.001067052,0.001448052,0.0002623173,0.00005228968,0.0000531348,0.1020898,0.0001132521,0.0100805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03416139,"threshold_uncertainty_score":0.06872511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0904232382622655,"score_gpt":0.3446667577445044,"score_spread":0.2542435194822389,"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."}}