{"id":"W3125804578","doi":"","title":"Modality for Scenario Analysis and Maximum Likelihood Allocation","year":2020,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Econometrics; Conditional probability distribution; Capital allocation line; Conditional expectation; Distribution (mathematics); Set (abstract data type); Mathematics; Statistics; Economics; Computer science; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007409024,0.0002496846,0.0007713041,0.001298359,0.0002096097,0.0007096796,0.0009364241,0.0004258055,0.00006408237],"category_scores_gemma":[0.003404778,0.000238239,0.0003212396,0.0008842353,0.0002020814,0.0001961069,0.0009865683,0.0007532641,0.00001165805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003221367,"about_ca_system_score_gemma":0.0006395883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002559436,"about_ca_topic_score_gemma":0.001287274,"domain_scores_codex":[0.9956976,0.0004377214,0.001147531,0.001519721,0.0006712594,0.000526185],"domain_scores_gemma":[0.9960514,0.001483489,0.000425283,0.001215477,0.0005239457,0.0003004476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001852939,0.00008245824,0.08168557,0.00003468753,0.0003026444,0.000004114932,0.0007062584,0.0911565,0.000025941,0.0004773839,0.0001997752,0.8251394],"study_design_scores_gemma":[0.0005499149,0.00008739011,0.06240466,0.00002296664,0.0001051555,0.000001317245,0.0006915478,0.7214682,0.00008422512,0.2006122,0.01356222,0.0004102045],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8765395,0.000550665,0.04921807,0.01994502,0.001214338,0.005233192,0.000791463,0.0001445868,0.04636316],"genre_scores_gemma":[0.9744927,0.01312113,0.01070303,0.000139892,0.0002180587,0.0002557694,0.0003004565,0.00003643928,0.0007325756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8247291,"threshold_uncertainty_score":0.9715105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09796591391454398,"score_gpt":0.4012534466663731,"score_spread":0.3032875327518291,"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."}}