{"id":"W4317212631","doi":"10.1079/onehealthcases.2023.0001","title":"Engaging an Interdisciplinary Team to Map the Current and Future Distribution of the Asian Longhorned Tick ( <i>Haemaphysalis longicornis</i> ) in North America: A One Health Approach to Risk Mapping and the Added Value of Citizen Science","year":2023,"lang":"en","type":"article","venue":"One Health Cases","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Science Centre for Human and Animal Health; Agriculture and Agri-Food Canada; Public Health Agency of Canada; University of Calgary","funders":"","keywords":"Tick; Geography; Government (linguistics); Haemaphysalis longicornis; Habitat; Distribution (mathematics); Public health; Haemaphysalis; Vector (molecular biology); Climate change; One Health; Political science; Ecology; Biology; Medicine; Ixodidae","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500042,0.0001849664,0.0004821272,0.0001864056,0.001108333,0.00002442558,0.0003176591,0.00004135164,0.00000279098],"category_scores_gemma":[0.0002711639,0.0001131648,0.00006233754,0.001540219,0.001034915,0.00008750086,0.0005842517,0.0004555277,0.000004381015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530492,"about_ca_system_score_gemma":0.0004125837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002702145,"about_ca_topic_score_gemma":0.001470078,"domain_scores_codex":[0.9971718,0.001231524,0.0005033771,0.0004547284,0.0001278341,0.0005107856],"domain_scores_gemma":[0.9985588,0.0003422769,0.0003853679,0.000512835,0.00008553871,0.0001151425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003129159,0.004114205,0.2056547,0.003594396,0.0004331389,0.000008349581,0.2093716,0.001282404,0.00281099,0.006718701,0.009755649,0.5531268],"study_design_scores_gemma":[0.00097394,0.0006124278,0.9829431,0.0002631659,0.0000399412,0.00004676713,0.01371442,0.00009674902,0.0001777666,0.0001588632,0.0008222573,0.0001506425],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821504,0.003484416,0.0003086889,0.01134669,0.0003212349,0.001619425,0.0006913028,0.00005238154,0.00002545754],"genre_scores_gemma":[0.9986855,0.0004306357,0.00004469527,0.0004961549,0.00006595538,0.000119878,0.0001383263,0.00001431812,0.000004532707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7772884,"threshold_uncertainty_score":0.8524515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031278861229344,"score_gpt":0.2958187457837215,"score_spread":0.275505957171428,"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."}}