{"id":"W1853471114","doi":"10.22621/cfn.v120i2.291","title":"Predicting Raccoon, <em>Procyon lotor</em>, Occurrence Through the Use of Microhabitat Variables","year":2006,"lang":"en","type":"article","venue":"The Canadian Field-Naturalist","topic":"Rabies epidemiology and control","field":"Immunology and Microbiology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Memphis; Tennessee Wildlife Resources Agency","keywords":"Univariate; Logistic regression; Abundance (ecology); Habitat; Jackknife resampling; Generalist and specialist species; Ecology; Snag; Generalized additive model; Statistics; Biology; Environmental science; Geography; Multivariate statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007708278,0.0003418551,0.000510884,0.00008528148,0.001102549,0.0000749478,0.0009778459,0.0007059669,0.0007588064],"category_scores_gemma":[0.001374493,0.0002158273,0.0002041571,0.0002619446,0.0006442581,0.0002485814,0.0001013169,0.001058388,0.0002200126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232539,"about_ca_system_score_gemma":0.000378343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1388412,"about_ca_topic_score_gemma":0.5410271,"domain_scores_codex":[0.9971935,0.0007608122,0.0006450511,0.000463881,0.00007113712,0.0008656179],"domain_scores_gemma":[0.9958453,0.002619374,0.00036362,0.0009108914,0.000202932,0.00005784137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004054363,0.00005330032,0.01257662,0.00005320193,0.0004581996,0.00002639356,0.00167531,0.0004504239,0.02484398,0.04124956,0.9162833,0.001924291],"study_design_scores_gemma":[0.001312349,0.0001184982,0.01392274,0.0001191132,0.0002746371,0.0001711084,0.0004614005,0.0001225342,0.006458243,0.002969324,0.9735269,0.0005431824],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9240016,0.0207617,0.0002258128,0.03491853,0.005468444,0.001851752,0.001399046,0.0001972024,0.01117595],"genre_scores_gemma":[0.9866476,0.00009203221,0.0000970652,0.006642404,0.0001962673,0.00005353051,0.0002668675,0.00001846981,0.005985788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4021859,"threshold_uncertainty_score":0.8801181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203126935489335,"score_gpt":0.2306397059253786,"score_spread":0.2103270123764451,"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."}}