{"id":"W2062174844","doi":"10.1007/s10144-013-0389-y","title":"Combining data from 43 standardized surveys to estimate densities of female American black bears by spatially explicit capture–recapture","year":2013,"lang":"en","type":"article","venue":"Population Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry","funders":"Ministry of Natural Resources","keywords":"Mark and recapture; Ursus; Statistics; Inference; Range (aeronautics); Biology; Ecology; Computer science; Mathematics; Population; Demography; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007644189,0.000496398,0.000526607,0.001466145,0.0004348263,0.0005721557,0.0006254278,0.0002817688,0.0004217415],"category_scores_gemma":[0.006883866,0.0005093334,0.0007063255,0.001430745,0.0004012255,0.0005853692,0.0007156834,0.0003543791,0.0001075832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005929763,"about_ca_system_score_gemma":0.0005289718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03580844,"about_ca_topic_score_gemma":0.08659836,"domain_scores_codex":[0.9969265,0.001819089,0.0001839976,0.0005822958,0.0003478311,0.0001403323],"domain_scores_gemma":[0.9939113,0.002157954,0.001618317,0.00105692,0.001126456,0.0001290303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006653423,0.00002671129,0.9866066,0.00002103916,0.000411906,0.00002199108,0.0001409759,0.004049724,0.0008003372,0.00004797947,0.0001330334,0.007673147],"study_design_scores_gemma":[0.000007556628,0.00007906937,0.9756691,0.00001285778,0.0002111404,0.00005784193,0.0002613945,0.02243793,0.0007533955,0.0001246216,0.0003666884,0.00001837986],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950703,0.00007830719,0.004177034,0.00001152065,0.000003491065,0.00002858318,0.0004019134,0.0000289302,0.0001999428],"genre_scores_gemma":[0.9944852,0.00005171379,0.004469452,0.00001263695,0.000004493655,0.00004426813,0.0008700406,0.000006579764,0.00005565663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03580844,"threshold_uncertainty_score":0.07120001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108946300107573,"score_gpt":0.2725258659983109,"score_spread":0.2514364029972351,"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."}}