{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007785934,0.001751647,0.001802008,0.002337545,0.0006046388,0.003460668,0.002011677,0.001606996,0.008581517],"category_scores_gemma":[0.0346384,0.0009487713,0.002265575,0.001988069,0.002484481,0.004580621,0.00365071,0.003555583,0.001014518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002377893,"about_ca_system_score_gemma":0.001270602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001368228,"about_ca_topic_score_gemma":0.000776033,"domain_scores_codex":[0.9956923,0.002877948,0.0001392092,0.0005393405,0.0005292299,0.0002219475],"domain_scores_gemma":[0.9831687,0.01396058,0.000943054,0.0009951076,0.000549167,0.0003833588],"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":[0.00008139406,0.00006380546,0.001085361,0.0001598486,0.0001760566,0.0001703541,0.0002503158,0.5557374,0.001139114,0.4007925,0.001543333,0.03880047],"study_design_scores_gemma":[0.000009000281,0.0000216456,0.0001642403,0.00002006577,0.00001171656,0.00003766852,0.00001911203,0.6820331,0.0002657088,0.3165352,0.0008661662,0.00001645658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004106938,0.0001754898,0.9931986,0.000224483,0.00001507982,0.00004618942,0.00007751327,0.00009973776,0.002055884],"genre_scores_gemma":[0.5992805,0.001000012,0.3917584,0.0003823618,0.0002517743,0.0009237968,0.0004832851,0.0003953041,0.005524625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008581517,"threshold_uncertainty_score":0.04117644,"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."}}