{"id":"W7116718655","doi":"10.2139/ssrn.5943836","title":"Highly Pathogenic Avian Influenza in Canada: Spatial Analysis of Ecological Hot and Cold Spots and Environmental Drivers","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Canada; Artificial Intelligence in Medicine (Canada); University of Toronto; York University","funders":"","keywords":"Waterfowl; Influenza A virus subtype H5N1; Outbreak; Population; Cold spot; Vegetation (pathology); Normalized Difference Vegetation Index; Highly pathogenic; Wildlife; Cluster (spacecraft)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003998072,0.000235732,0.0002956384,0.001591564,0.001274484,0.001307191,0.0006528893,0.0003582714,0.001828814],"category_scores_gemma":[0.001685434,0.000199598,0.0004511825,0.005040177,0.0005978907,0.0003295078,0.000870423,0.0003079368,0.0001395497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008443143,"about_ca_system_score_gemma":0.01365569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9879285,"about_ca_topic_score_gemma":0.9923332,"domain_scores_codex":[0.9996313,0.00003955203,0.0000210494,0.00007535579,0.00008240011,0.0001503478],"domain_scores_gemma":[0.9985375,0.0002255128,0.0002657272,0.00004840192,0.0006256561,0.000297244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008525547,0.00001268767,0.9924976,0.00002547744,0.00008435926,0.00005338988,0.0003693208,0.001283054,0.000454257,0.00020409,0.000678192,0.004252221],"study_design_scores_gemma":[0.000002651429,0.000006170963,0.99698,0.00001057158,0.00002368251,0.0000253163,0.001000066,0.001401736,0.0000396808,0.00004498354,0.0004579383,0.000007161891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957339,0.0003975072,0.0001872393,0.000135469,0.000005193775,0.000009234795,0.002685624,0.000009708071,0.0008359628],"genre_scores_gemma":[0.9981026,0.0001918874,0.0002156822,0.0000155055,0.000003299839,0.000004367551,0.001049808,0.000004126687,0.0004127429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01207155,"threshold_uncertainty_score":0.06125963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010691278740937,"score_gpt":0.2095453902993415,"score_spread":0.1994384775119322,"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."}}