{"id":"W2765550888","doi":"10.1038/s41593-017-0008-x","title":"Elucidating the underlying components of food valuation in the human orbitofrontal cortex","year":2017,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":217,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Aging","funders":"National Institute of Mental Health","keywords":"Orbitofrontal cortex; Valuation (finance); Neuroscience; Psychology; Multivariate statistics; Prefrontal cortex; Biology; Cognitive psychology; Computer science; Cognition; Machine learning; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001018979,0.00006744857,0.0001014492,0.000007206065,0.001065644,0.0001579379,0.0008853393,0.00005717523,0.00001058127],"category_scores_gemma":[0.001172822,0.00001815223,0.00004863067,0.0001802735,0.0002341555,0.0001155943,0.0000688802,0.0003216662,6.13618e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006032556,"about_ca_system_score_gemma":0.000002827592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001227621,"about_ca_topic_score_gemma":0.0004017962,"domain_scores_codex":[0.9987801,0.0003034163,0.000162592,0.000212254,0.0003976074,0.0001440189],"domain_scores_gemma":[0.9990656,0.0005547391,0.0001844333,0.0001387611,0.00003595269,0.00002048758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000002671545,0.00003484528,0.00716884,0.000001332047,7.917056e-7,0.000001805144,0.00007419106,0.000007361491,0.9711578,0.008302961,0.00001200245,0.01323538],"study_design_scores_gemma":[0.00004451619,0.0001102174,0.9910323,0.000007994372,0.000006055031,0.000002906482,0.0001639131,0.001662288,0.002163643,0.004587817,0.0001734458,0.00004491741],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977948,0.00002347005,0.0001070654,0.001071411,0.000162327,0.0001142374,0.00000891774,0.000004820666,0.0007129472],"genre_scores_gemma":[0.9992788,0.000002582471,0.0001081394,0.0005442955,0.00004462339,0.000002997704,0.000003348483,3.187122e-7,0.00001491906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9838635,"threshold_uncertainty_score":0.8196181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.192469340653037,"score_gpt":0.3873999921289087,"score_spread":0.1949306514758717,"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."}}