{"id":"W7006409016","doi":"","title":"Translating Probability Density Functions: From R to BUGS and Back Again","year":2013,"lang":"en","type":"article","venue":"Lincoln (University of Nebraska)","topic":"Plant Ecology and Taxonomy Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Energy Biosciences Institute","keywords":"Bayesian probability; Software bug; Software; Bayesian inference; Task (project management); Inference; Statistical inference; Markov chain Monte Carlo; Interpretation (philosophy)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008042496,0.0000721374,0.0001560616,0.000009780444,0.0002907352,0.000009652153,0.0001032807,0.00007729854,0.0009996946],"category_scores_gemma":[0.0000235618,0.00004046055,0.00004292457,0.0001339881,0.00009332871,0.0001415303,0.0000897453,0.00006818035,0.0002189079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001347208,"about_ca_system_score_gemma":0.000005621935,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002974753,"about_ca_topic_score_gemma":0.01944741,"domain_scores_codex":[0.9994731,0.00004744668,0.00006995842,0.0002245868,0.00005571562,0.0001291806],"domain_scores_gemma":[0.9995717,0.0002165964,0.0000457404,0.0000357532,0.00005206445,0.00007811269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001128027,0.0001922745,0.7664362,0.00001920195,0.0001042673,0.00000669581,0.001057628,0.00001241567,0.0391267,0.0001140023,0.005134422,0.1876834],"study_design_scores_gemma":[0.000131641,0.0001017468,0.9890927,0.00001043024,0.00001366784,8.522982e-7,0.001752533,0.00006659263,0.00004839054,0.001005585,0.007682495,0.00009334815],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925767,0.00004277095,0.00005744719,0.005187778,0.00003886726,0.0002908459,0.00008972161,0.00002525402,0.001690614],"genre_scores_gemma":[0.997018,0.000009925929,0.002106528,0.0001355679,0.00003524231,9.11079e-7,0.0000236657,2.408571e-7,0.000669924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2226566,"threshold_uncertainty_score":0.9999135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120049004683737,"score_gpt":0.1552574428280714,"score_spread":0.1340569527812341,"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."}}