{"id":"W577779381","doi":"","title":"Bayesian inference and maximum entropy methods in science and engineering : 31st international workshop on bayesian inference and maximum entropy mthods in science and engineering , Waterloo, Ontario, Canada, 9-16 July 2011","year":2011,"lang":"en","type":"book","venue":"American Institute of Physics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principle of maximum entropy; Inference; Bayesian inference; Science and engineering; Bayesian probability; Probabilistic logic; Entropy (arrow of time); Computer science; Statistical inference; Graphical model; Data science; Artificial intelligence; Mathematics; Statistics; Engineering; Physics; Engineering ethics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006811111,0.0005382289,0.0006666185,0.0006369145,0.0002044865,0.0003378264,0.001263481,0.00009334998,0.000002802321],"category_scores_gemma":[0.0001112412,0.0005466039,0.00003180723,0.0005011405,0.002098121,0.000740169,0.001064084,0.0007756079,3.948968e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008820069,"about_ca_system_score_gemma":0.002855709,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1408622,"about_ca_topic_score_gemma":0.07843298,"domain_scores_codex":[0.9967795,0.00003265767,0.0005602848,0.001211632,0.000729378,0.0006865307],"domain_scores_gemma":[0.9981375,0.0002520827,0.0003578395,0.0006254099,0.0002481898,0.000379018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003609382,0.00008658159,0.001841569,0.0001597744,0.00005934506,0.00006402479,0.003076689,0.001594453,0.01166525,0.3006462,0.0001157198,0.6806543],"study_design_scores_gemma":[0.005025428,0.001543196,0.07929241,0.007275794,0.0002490258,0.0002705423,0.0003113103,0.635269,0.02301068,0.1269743,0.1112403,0.00953804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05887844,0.0002604879,0.9185206,0.0004023447,0.001478043,0.001564006,0.00005621929,0.0001430609,0.01869686],"genre_scores_gemma":[0.8213252,0.0002194497,0.1762965,0.0002585224,0.0001495789,0.0001109238,0.000009128175,0.00005267034,0.001578018],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7624467,"threshold_uncertainty_score":0.9996985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572222250705431,"score_gpt":0.2591005200633257,"score_spread":0.2433782975562714,"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."}}