{"id":"W2018189188","doi":"10.1073/pnas.1219674110","title":"Limits in decision making arise from limits in memory retrieval","year":2013,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Topic Modeling","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institute of Mental Health","keywords":"Computer science; Idealization; Probabilistic logic; Process (computing); Machine learning; Set (abstract data type); Artificial intelligence; Noise (video); Rationality; Contrast (vision); Sample (material); Test (biology)","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":[0.009320477,0.000709784,0.001298587,0.001250672,0.0007877293,0.006492272,0.002202292,0.002266856,0.002952143],"category_scores_gemma":[0.09583497,0.001126352,0.001014543,0.0009706593,0.007832335,0.007958222,0.003650141,0.003444972,0.0009215008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001870411,"about_ca_system_score_gemma":0.001136378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002314538,"about_ca_topic_score_gemma":0.001096383,"domain_scores_codex":[0.9903721,0.002893846,0.0008916373,0.002552628,0.002546576,0.0007432983],"domain_scores_gemma":[0.9269068,0.05295514,0.005727692,0.01019474,0.002685073,0.001530612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001054227,0.0004947432,0.04722491,0.0006484261,0.0007012803,0.001101726,0.006191955,0.0823158,0.02120104,0.6223238,0.003908862,0.2128332],"study_design_scores_gemma":[0.00006859194,0.0001341293,0.01394881,0.00007388192,0.00005736583,0.0002870672,0.0006142572,0.05116348,0.003380621,0.9275694,0.002609303,0.00009302171],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5848722,0.003031558,0.357119,0.008057047,0.0001009168,0.0001166565,0.0003723088,0.0005618468,0.04576847],"genre_scores_gemma":[0.9666557,0.0005313539,0.03028234,0.0006723563,0.00006562211,0.0001358818,0.0001677267,0.00009229399,0.001396743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009320477,"threshold_uncertainty_score":0.04929203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05956730408590526,"score_gpt":0.3109434555717325,"score_spread":0.2513761514858272,"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."}}