{"id":"W3022460099","doi":"10.48550/arxiv.2005.02950","title":"Modality for Scenario Analysis and Maximum Likelihood Allocation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Econometrics; Conditional probability distribution; Capital allocation line; Distribution (mathematics); Conditional expectation; Set (abstract data type); Mathematics; Statistics; Computer science; Economics; Microeconomics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001062979,0.0002496751,0.0005432608,0.000631468,0.000201567,0.0002786375,0.0007644406,0.0003067471,0.00006860571],"category_scores_gemma":[0.0004601749,0.0002499941,0.0004007016,0.001983269,0.00009971848,0.0002838511,0.0006057189,0.0002518558,0.00004038314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008545093,"about_ca_system_score_gemma":0.000192228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003820239,"about_ca_topic_score_gemma":0.0003845206,"domain_scores_codex":[0.9975418,0.0001865809,0.0004244981,0.001367149,0.0002389787,0.0002410219],"domain_scores_gemma":[0.997427,0.0003386812,0.0005301137,0.0008853091,0.0005719935,0.0002469188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002751882,0.0001039573,0.1725313,0.00003394473,0.0008078566,0.00002852626,0.0005986635,0.7919251,0.0000236653,0.0191799,0.001377651,0.01311423],"study_design_scores_gemma":[0.0002940297,0.00002942534,0.01914901,0.000005813864,0.0007623145,3.281277e-7,0.0001681954,0.63136,0.00003126177,0.3470088,0.0009553974,0.0002354714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2282766,0.00004334308,0.7694346,0.0007896679,0.000193102,0.0003872463,0.00009744012,0.00006023408,0.00071782],"genre_scores_gemma":[0.995377,0.0005922188,0.002870103,0.000123486,0.00007927768,0.000001913791,0.000173071,0.0000135011,0.0007693982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7671005,"threshold_uncertainty_score":0.9999952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1660114759263384,"score_gpt":0.2716857002815896,"score_spread":0.1056742243552511,"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."}}