{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003617381,0.0001011528,0.0001482045,0.00003876346,0.0001230989,0.00008734861,0.0002757424,0.00007315393,0.000001158367],"category_scores_gemma":[0.00005379186,0.00009487657,0.00005065179,0.0002047606,0.00001445113,0.0004531869,0.00005595589,0.0001109289,0.000009194633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005041762,"about_ca_system_score_gemma":0.00004316205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005461507,"about_ca_topic_score_gemma":0.00001019692,"domain_scores_codex":[0.999073,0.00003603058,0.0001568296,0.0003085689,0.0002137591,0.0002118449],"domain_scores_gemma":[0.9991508,0.00006214954,0.00006115981,0.0003887222,0.0002036939,0.000133451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001643029,0.002039907,0.00005151851,0.0003592483,0.0004421145,0.000005239178,0.002876792,0.002795385,0.05653655,0.558432,0.001636802,0.3746601],"study_design_scores_gemma":[0.0003600489,0.0001120138,0.000009398203,0.00006656603,0.0000435804,0.000002452829,0.00001279817,0.634489,0.01275535,0.3509791,0.0009857928,0.0001839265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001499221,0.0003589237,0.9958686,0.0005372475,0.0002043509,0.0002612782,0.000009130726,0.0001930592,0.001068153],"genre_scores_gemma":[0.1894778,0.000007703448,0.8093593,0.0001148592,0.0001562302,0.00008467172,0.00000286647,0.00001086338,0.0007856547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6316936,"threshold_uncertainty_score":0.3868954,"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."}}