{"id":"W2889404271","doi":"10.1111/2041-210x.13082","title":"Model selection with overdispersed distance sampling data","year":2018,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry","funders":"Max-Planck-Gesellschaft; Ministère de l'Enseignement Supérieur et de la Recherche; Ministère de l'Enseignement Supérieur et de la Recherche Scientifique; Robert Bosch Stiftung; University of St Andrews; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Overdispersion; Overfitting; Model selection; Statistics; Akaike information criterion; Goodness of fit; Estimator; Selection (genetic algorithm); Data set; Computer science; Set (abstract data type); Mathematics; Sampling (signal processing); Bayesian information criterion; Count data; Poisson distribution; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009442788,0.00006807898,0.00009053001,0.00003043829,0.0002121654,0.00000534127,0.0001062921,0.0001141396,0.0001255759],"category_scores_gemma":[0.0001301702,0.00006243213,0.000005323096,0.0001879701,0.0003268017,0.0003122491,0.0001147861,0.0001145344,0.00002188668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148484,"about_ca_system_score_gemma":0.00001881627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000913303,"about_ca_topic_score_gemma":0.009095216,"domain_scores_codex":[0.9991602,0.0001961668,0.000114777,0.0003135907,0.00004302178,0.0001722132],"domain_scores_gemma":[0.9996381,0.0001111379,0.00005124313,0.0001678409,0.000007484192,0.0000241717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008507163,0.0000272568,0.9927682,0.000001847161,0.000003954835,2.697696e-7,0.0001011742,0.00253431,0.0007661,0.0008002986,0.0002456569,0.002665858],"study_design_scores_gemma":[0.0001655392,0.00006003301,0.6533027,0.000002298768,0.000007178814,0.000005755961,0.00002499534,0.3402253,0.00002037339,0.005887597,0.0002443803,0.00005381349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5200577,0.000007374784,0.4787622,0.0001795187,0.00006678014,0.00006492732,0.000001372682,0.00001397562,0.0008460425],"genre_scores_gemma":[0.6981577,0.000004743079,0.3014625,0.0002005612,0.00002790541,0.00001029847,0.000006382496,0.000003347822,0.0001265931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3394655,"threshold_uncertainty_score":0.5075344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05782814142344339,"score_gpt":0.3528964861043608,"score_spread":0.2950683446809174,"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."}}