{"id":"W2156524460","doi":"10.1371/journal.pone.0118726","title":"Mixture Models for Distance Sampling Detection Functions","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Raincoast Conservation Foundation","keywords":"Sampling (signal processing); Computer science; Parametric statistics; Monotonic function; Set (abstract data type); Function (biology); Covariate; Sample size determination; Key (lock); Distance sampling; Selection (genetic algorithm); Model selection; Statistics; Data mining; Algorithm; Mathematics; Artificial intelligence; Machine learning; Transect; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01448609,0.002015037,0.002479066,0.003528108,0.0009545243,0.003120729,0.007325129,0.003233417,0.004415034],"category_scores_gemma":[0.05054922,0.00161353,0.003642185,0.002765224,0.003548197,0.006787659,0.003256142,0.004453628,0.0016785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003030524,"about_ca_system_score_gemma":0.001316102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006371517,"about_ca_topic_score_gemma":0.004633211,"domain_scores_codex":[0.992893,0.004141325,0.0002773167,0.001287566,0.001082591,0.0003181559],"domain_scores_gemma":[0.9697227,0.02393291,0.002161458,0.001961426,0.001821669,0.0003998607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001341383,0.00005724221,0.00309709,0.0001798842,0.0001477425,0.0001079551,0.0003501812,0.522047,0.001010206,0.4194835,0.002342464,0.05104252],"study_design_scores_gemma":[0.0000140875,0.00001966763,0.0003069586,0.00002393837,0.00002394853,0.00006023301,0.00001921161,0.9142553,0.0002629006,0.08297369,0.002010275,0.00002980593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002831897,0.0001583133,0.9962348,0.0001310275,0.00001978093,0.00003392891,0.00006325849,0.0001400536,0.000386893],"genre_scores_gemma":[0.3095503,0.001078894,0.6772792,0.000440257,0.0002304918,0.0009474281,0.001015949,0.0004718452,0.008985711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01448609,"threshold_uncertainty_score":0.07661068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1432912078368894,"score_gpt":0.2796155648453607,"score_spread":0.1363243570084713,"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."}}