{"id":"W1968707191","doi":"10.1111/j.1467-9876.2008.00652.x","title":"Using Bayesian Inference to Understand the Allocation of Resources Between Sexual and Asexual Reproduction","year":2009,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Markov chain Monte Carlo; Bayesian inference; Computer science; Bayesian probability; Statistical inference; Prior probability; Range (aeronautics); Artificial intelligence; Machine learning; Ecology; Mathematics; Statistics; Biology; Engineering","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":[0.0005774445,0.0001353029,0.0002417104,0.000006685303,0.0004339591,0.00007068957,0.0002059607,0.00007546755,0.00003766599],"category_scores_gemma":[0.0002473656,0.00004822013,0.00004730089,0.0002069768,0.0003089769,0.00006782794,0.0000622437,0.0002558276,5.422507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005835856,"about_ca_system_score_gemma":0.00002348749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000889965,"about_ca_topic_score_gemma":0.00002759274,"domain_scores_codex":[0.9986107,0.00009231183,0.0004638246,0.0002275546,0.0004207191,0.0001849075],"domain_scores_gemma":[0.9989741,0.0003010973,0.0003552696,0.00009169435,0.000174632,0.0001032264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001461747,0.0005102826,0.01921222,0.0001314653,0.0003084193,0.000006596125,0.01060066,0.00387716,0.3320592,0.07441038,0.01218634,0.5452355],"study_design_scores_gemma":[0.0002321612,0.001687104,0.9618446,0.00002920758,0.0003033657,0.00002712009,0.01073101,0.0005022202,0.001917357,0.02123859,0.001220834,0.0002664384],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9118204,0.00005193818,0.08460026,0.002854336,0.000132054,0.0002389632,0.0002449158,0.00001009491,0.00004708231],"genre_scores_gemma":[0.9853575,0.00001093462,0.01404304,0.00008464208,0.0004148275,6.927024e-7,0.000008824533,0.000001702764,0.0000778745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9426324,"threshold_uncertainty_score":0.3337706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0389356829272819,"score_gpt":0.277261171259499,"score_spread":0.2383254883322171,"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."}}