{"id":"W4366503682","doi":"10.1139/facets-2022-0137","title":"The influence of sociodemographic and environmental factors on wildlife carcass submissions in urban areas: Opportunities for increasing equitable and representative wildlife health surveillance","year":2023,"lang":"en","type":"article","venue":"FACETS","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canadian Wildlife Health Cooperative; University of Guelph","keywords":"Wildlife; Geography; Environmental health; Poisson regression; Public health; Wildlife disease; Environmental resource management; Business; Environmental planning; Medicine; Ecology; Environmental science; Population; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001067218,0.0001250355,0.0001831133,0.00005787317,0.0005407595,0.00001814937,0.0001077495,0.00006399309,0.00001317895],"category_scores_gemma":[0.0002449372,0.0001002747,0.00002607593,0.0001629558,0.0005423176,0.0001768036,0.0001685724,0.0001108547,0.000002733764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007191675,"about_ca_system_score_gemma":0.0000291131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007521479,"about_ca_topic_score_gemma":0.0004205865,"domain_scores_codex":[0.9986866,0.0002516859,0.0002508805,0.0002703184,0.0001735664,0.0003669899],"domain_scores_gemma":[0.9985892,0.0009940283,0.0001525609,0.0001547028,0.000004071699,0.000105389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004659791,0.00002115452,0.9939623,0.00001004619,0.000009823126,0.000001155102,0.00143648,0.0004114555,0.0001486806,0.00006842583,0.003496074,0.0003877449],"study_design_scores_gemma":[0.0002976627,0.0001103672,0.9926968,0.00002603221,0.000002796643,0.00000177819,0.004646112,0.0004770262,0.00001832881,0.0002896032,0.001337335,0.00009612314],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948196,0.00008883476,0.000003799587,0.004472475,0.00002810174,0.0004014743,0.0001014843,0.0000220228,0.00006219756],"genre_scores_gemma":[0.9983876,0.0005219401,0.00002110606,0.000703247,0.000006712443,0.00004797094,0.0000529942,0.000009730781,0.0002487232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003769228,"threshold_uncertainty_score":0.415914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04140334978111058,"score_gpt":0.2768815159776278,"score_spread":0.2354781661965173,"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."}}