{"id":"W2059157883","doi":"10.1016/j.prevetmed.2008.03.007","title":"Assessing a landscape barrier using genetic simulation modelling: Implications for raccoon rabies management","year":2008,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Ministry of Natural Resources and Forestry; Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources","keywords":"Wildlife; Rabies; Population; Genetic diversity; Geography; Ecology; Gene flow; Wildlife management; Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002077732,0.0001102296,0.000130303,0.00005462035,0.0003693605,0.000007188503,0.00009995195,0.0000468696,0.0003360884],"category_scores_gemma":[0.00002732937,0.0001018693,0.00003821576,0.0001502485,0.0001445111,0.0003047959,0.00007521397,0.00004944214,0.000013087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006574858,"about_ca_system_score_gemma":0.000007916703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001438954,"about_ca_topic_score_gemma":9.3561e-7,"domain_scores_codex":[0.9991635,0.00007558354,0.0002260702,0.0002629011,0.00009950442,0.0001724498],"domain_scores_gemma":[0.9995199,0.0001389621,0.0001089197,0.000165127,0.00001815205,0.00004891293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001329409,0.0001176759,0.4463433,0.0000504837,0.00006091209,0.00001691117,0.0008639412,0.5423498,0.00352594,0.00007419915,0.0005775252,0.005886347],"study_design_scores_gemma":[0.0006672373,0.0001782276,0.6771241,0.00003434802,0.00005888236,0.00003390616,0.00009424794,0.3188364,0.00001217135,0.00141899,0.001434954,0.0001064933],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6594664,0.00005699768,0.3391645,0.0002744599,0.00007794431,0.0003570335,0.000001757313,0.00002174319,0.0005791216],"genre_scores_gemma":[0.9796623,0.00004065377,0.01944141,0.000207589,0.00008307917,0.00009709267,0.00002293092,0.00001211656,0.0004328391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3201958,"threshold_uncertainty_score":0.4154109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229329303691117,"score_gpt":0.3465724917488799,"score_spread":0.2236395613797682,"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."}}