{"id":"W2289900132","doi":"10.1111/brv.12254","title":"Assessing variation in life‐history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists","year":2016,"lang":"en","type":"article","venue":"Biological reviews/Biological reviews of the Cambridge Philosophical Society","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd; Conseil National de la Recherche Scientifique; Fonds Québécois de la Recherche sur la Nature et les Technologies; Alberta Conservation Association","keywords":"Akaike information criterion; Variation (astronomy); Population; Econometrics; Ecology; Model selection; Mixture model; Statistics; Mathematics; Biology; Demography; Sociology","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":[0.01683155,0.00460785,0.003695568,0.007792546,0.0008620909,0.003990839,0.006999684,0.003982769,0.01449047],"category_scores_gemma":[0.05187898,0.003621744,0.003772178,0.003968721,0.002111665,0.004592998,0.002887429,0.00772617,0.009032884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080871,"about_ca_system_score_gemma":0.001530854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004149468,"about_ca_topic_score_gemma":0.008604582,"domain_scores_codex":[0.9951573,0.003150111,0.000531251,0.0004393325,0.0006500097,0.00007197659],"domain_scores_gemma":[0.9522635,0.04080976,0.001527773,0.001764182,0.002960565,0.0006743217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002997781,0.0008237245,0.008900137,0.003538244,0.001104054,0.001040087,0.002747172,0.08033097,0.01208576,0.05823636,0.2884292,0.5424646],"study_design_scores_gemma":[0.0003618219,0.0004515594,0.008015227,0.001950172,0.0003171139,0.001517524,0.001081193,0.4492616,0.0037704,0.2233876,0.3092933,0.0005925498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001033404,0.002215328,0.987357,0.00137246,0.0001544061,0.0003734398,0.001105581,0.004857976,0.001530362],"genre_scores_gemma":[0.003234192,0.001340649,0.9919268,0.0002997637,0.0001088947,0.0008097769,0.0004764436,0.0006984062,0.001105175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01683155,"threshold_uncertainty_score":0.08901477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161481306643614,"score_gpt":0.3551395078358698,"score_spread":0.1389913771715083,"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."}}