{"id":"W2911336415","doi":"10.1002/ece3.4938","title":"Estimating feral cat densities using distance sampling in an urban environment","year":2019,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distance sampling; Transect; Windsor; Population density; Population; Geography; Population size; Feral cat; Wildlife; Sampling (signal processing); Ecology; Abundance (ecology); Forestry; Biology; Demography; Predation; Felis catus","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008207303,0.00006626327,0.0000833293,0.00002557103,0.00009118277,0.000008219818,0.00002617902,0.00007073261,0.00001900161],"category_scores_gemma":[0.00001634296,0.00007088549,0.00001349737,0.00001318487,0.00005143472,0.000009960768,0.00004803302,0.0000480453,0.00001065803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007157105,"about_ca_system_score_gemma":0.00001132533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002568789,"about_ca_topic_score_gemma":0.0003387812,"domain_scores_codex":[0.9995165,0.00003839771,0.000103996,0.0001872323,0.00002669706,0.0001271378],"domain_scores_gemma":[0.9998427,0.000009876264,0.00004092661,0.000076833,0.00001179241,0.00001786145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004826202,0.00002141575,0.7262845,0.000008506958,0.00001065853,7.658973e-7,0.0002103679,0.006258133,0.2669373,0.000166874,0.00002501487,0.00002816171],"study_design_scores_gemma":[0.0003058005,0.000247139,0.9641711,0.00001377248,0.000009725944,0.000014789,0.0006054251,0.03219501,0.001685294,0.0001466776,0.0004543033,0.0001509334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970404,0.000225192,0.002377804,0.00002335717,0.0001898314,0.00007254462,0.000002023236,0.000004778493,0.00006410253],"genre_scores_gemma":[0.9971553,0.000008692372,0.00250608,0.00004646837,0.00009750459,0.000005760786,0.00001556067,0.000005453079,0.000159163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.265252,"threshold_uncertainty_score":0.2890626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973738075143383,"score_gpt":0.3070191139699111,"score_spread":0.2872817332184773,"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."}}