{"id":"W2138813426","doi":"10.1214/11-bjps147","title":"On default priors and approximate location models","year":2011,"lang":"en","type":"article","venue":"Brazilian Journal of Probability and Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Mathematics; Consistency (knowledge bases); Estimator; Bayes' theorem; Inference; Interpretation (philosophy); Applied mathematics; Posterior probability; Confidence interval; Mathematical statistics; Statistical inference; Statistics; Bayesian probability; Computer science; Artificial intelligence; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001058482,0.0001377474,0.0003043891,0.00005562662,0.00008183854,0.00003357374,0.0000979914,0.00006935488,0.00005462513],"category_scores_gemma":[0.003747022,0.0001057551,0.00002383959,0.00007704878,0.0002570796,0.0001199524,0.00003041524,0.0002231442,0.000001096872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002360183,"about_ca_system_score_gemma":0.00006724522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006975967,"about_ca_topic_score_gemma":0.000007908677,"domain_scores_codex":[0.9987203,0.0001818048,0.0005586737,0.0001628731,0.0002134915,0.0001627929],"domain_scores_gemma":[0.9978975,0.001096168,0.0002994696,0.0001584193,0.0003709997,0.000177413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000130915,0.0001547178,0.0003509168,0.0003125174,0.00001957597,0.000009921591,0.001496586,0.00001224437,0.00001056469,0.9601166,0.0002195031,0.03716588],"study_design_scores_gemma":[0.0003540485,0.0005705468,0.003969579,0.0001083952,0.0000530232,0.00005131356,0.00013351,0.014211,0.00007129005,0.9803507,0.00001183362,0.0001147004],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06754064,0.00006546098,0.931203,0.00004394382,0.00007402169,0.0001715925,0.00006079382,0.000008940759,0.0008316144],"genre_scores_gemma":[0.3013422,0.00003362878,0.698544,0.00003636466,0.00001462458,0.000002091313,5.483994e-7,0.000008358883,0.00001810079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2338016,"threshold_uncertainty_score":0.4485806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1413404826612175,"score_gpt":0.3310141565555768,"score_spread":0.1896736738943593,"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."}}