{"id":"W759726671","doi":"","title":"Piecewise bounds for estimating bernoulli-logistic latent Gaussian models","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Piecewise; Mathematics; Minimax; Applied mathematics; Upper and lower bounds; Quadratic equation; Bernoulli's principle; Gaussian; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006373051,0.001628118,0.001317486,0.001543206,0.0008526625,0.00251211,0.003457077,0.001932893,0.00372978],"category_scores_gemma":[0.05183195,0.001104408,0.000890923,0.002044732,0.002065165,0.006245706,0.004258888,0.004088196,0.0009840165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0021266,"about_ca_system_score_gemma":0.001039286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00340394,"about_ca_topic_score_gemma":0.00265722,"domain_scores_codex":[0.9967799,0.001863408,0.0001306324,0.0004186117,0.0006040544,0.0002034773],"domain_scores_gemma":[0.9818153,0.01477315,0.0009571165,0.001373634,0.0008337561,0.0002469403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002076462,0.00003645557,0.001824388,0.0002050126,0.00007355005,0.00008894856,0.0002280667,0.7555436,0.00259955,0.15883,0.001496338,0.07886641],"study_design_scores_gemma":[0.000005868624,0.00002541732,0.0002713631,0.00002784596,0.00001118681,0.00003019326,0.00002003979,0.9362599,0.0008309556,0.06168005,0.0008212786,0.00001587009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004315989,0.0004108109,0.994283,0.0001082584,0.00001206029,0.00001391224,0.00005316655,0.0002015781,0.0006012598],"genre_scores_gemma":[0.3992488,0.00177503,0.5940216,0.000240189,0.0001347622,0.0002626363,0.000787348,0.0005168131,0.003012822],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006373051,"threshold_uncertainty_score":0.03370428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1019456910048756,"score_gpt":0.276473512577154,"score_spread":0.1745278215722784,"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."}}