{"id":"W2067883763","doi":"10.1111/j.1541-0420.2008.01013.x","title":"Bayesian Estimation of Inverse Dose Response","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Maximum a posteriori estimation; Posterior probability; Bayesian probability; Bayesian inference; Mathematics; Posterior predictive distribution; Statistics; Prior probability; A priori and a posteriori; Computer science; Bayes estimator; Inverse problem; Bayesian linear regression; Algorithm; Maximum likelihood","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005490692,0.0001097323,0.0002505273,0.003488946,0.00009705705,0.00003928715,0.0005290622,0.0000964971,0.0003013355],"category_scores_gemma":[0.02513576,0.00008852012,0.0001062971,0.01351512,0.0002364187,0.0003108772,0.0001212398,0.00006547537,0.0003306455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007565884,"about_ca_system_score_gemma":0.0001162183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002094564,"about_ca_topic_score_gemma":2.753853e-7,"domain_scores_codex":[0.9967359,0.0006487875,0.0006089654,0.0003131198,0.001516037,0.0001772397],"domain_scores_gemma":[0.9953295,0.003431424,0.000279023,0.0005745312,0.0002456167,0.0001398824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003130338,0.0009219075,0.01928584,0.00001706555,0.00004206916,0.0001835502,0.003596056,0.003486958,0.521722,0.001292013,0.04290957,0.4034127],"study_design_scores_gemma":[0.002709904,0.001916331,0.2719055,0.0000248324,0.00002572509,0.0001704503,0.001143818,0.2378427,0.4486875,0.008078442,0.02667596,0.0008187869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7203886,0.0002102626,0.2762051,0.000106271,0.0003620235,0.0001626405,0.00001517455,0.00003870911,0.002511235],"genre_scores_gemma":[0.6680736,0.00001099337,0.3309211,0.000056488,0.00001223514,0.000002776216,0.00000115738,0.000007333646,0.0009143303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4025939,"threshold_uncertainty_score":0.9830759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.262967311778844,"score_gpt":0.4629643026311734,"score_spread":0.1999969908523295,"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."}}