{"id":"W4394410253","doi":"10.6084/m9.figshare.20277778","title":"Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian; Statistical physics; Scalability; Mixture model; Computer science; Mathematics; Artificial intelligence; Physics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000768492,0.000370155,0.0003780468,0.0003807817,0.0002949736,0.0005849127,0.00261883,0.0001877795,0.203092],"category_scores_gemma":[0.0001778753,0.0003730566,0.0001124954,0.001392302,0.000007221103,0.0006603628,0.00198617,0.0005964892,0.001398403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002465786,"about_ca_system_score_gemma":0.0004846213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007555876,"about_ca_topic_score_gemma":0.0001305388,"domain_scores_codex":[0.9974965,0.00007125149,0.0004727226,0.0008673567,0.0004862929,0.0006058874],"domain_scores_gemma":[0.9980676,0.00005396578,0.0002050311,0.001294755,0.0001133421,0.0002652706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002212346,0.0001202438,1.893042e-7,0.0002326906,0.000007508046,0.00006305632,0.00004977782,0.0003917767,8.290338e-7,0.0004092539,0.9973396,0.001382874],"study_design_scores_gemma":[0.0001878147,0.00009718257,0.0001730155,0.001161066,0.000009000079,0.0000506103,0.00001246646,0.008580247,0.00001264805,0.00223321,0.9868639,0.0006188533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[4.789038e-7,0.00009050134,0.004310769,0.000814645,0.0001262699,0.0006094456,0.9936531,0.0001083753,0.0002864795],"genre_scores_gemma":[0.0001143615,0.000005280655,0.002165932,0.0003201563,0.00004167991,0.001683722,0.9954751,0.00001607496,0.0001776815],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2016936,"threshold_uncertainty_score":0.9998721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375250700435088,"score_gpt":0.2639648428540332,"score_spread":0.2302123358496823,"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."}}