{"id":"W2168503562","doi":"10.1109/allerton.2010.5706887","title":"Volatility and efficiency in markets with friction","year":2010,"lang":"en","type":"article","venue":"","topic":"Electric Power System Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Volatility (finance); Market efficiency; Stochastic differential equation; Economics; Differential game; Function (biology); Econometrics; Game theory; Mathematical optimization; Computer science; Mathematical economics; Financial economics; Mathematics; Applied mathematics","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":[0.00009917016,0.00004135047,0.00004281239,0.00005118076,0.00001067327,0.00001030629,0.00002007367,0.00003293603,0.00003045367],"category_scores_gemma":[0.00001036334,0.00003386972,0.000002812389,0.0001684602,0.000005304561,0.00007666414,0.000002902182,0.0000793846,0.000002178723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001227907,"about_ca_system_score_gemma":0.000003959417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001866519,"about_ca_topic_score_gemma":0.000235515,"domain_scores_codex":[0.9997457,0.000005789714,0.00006480963,0.0000653606,0.00004392425,0.00007441071],"domain_scores_gemma":[0.9998804,0.00001586893,0.000005561877,0.0000697823,0.000009879472,0.0000184505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007044204,0.000145404,0.87906,0.0002781989,0.00003340168,0.00001254193,0.001535914,0.03354378,0.04550082,0.002856026,0.001222974,0.03574047],"study_design_scores_gemma":[0.0001465399,0.00001136101,0.1418569,0.000003993388,0.000001218482,0.000006629438,0.000006178132,0.8568858,0.0008883442,0.00001131176,0.0001275975,0.00005417213],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8991339,0.0000237368,0.07580455,0.000008195486,0.00009239753,0.00008279285,1.571501e-7,0.0001200523,0.02473421],"genre_scores_gemma":[0.9981145,0.000003320987,0.00178747,0.000002479218,0.000007059293,0.000004340203,5.833488e-7,0.000005689613,0.00007454235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.823342,"threshold_uncertainty_score":0.1381167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001808098797357905,"score_gpt":0.1606932634286251,"score_spread":0.1588851646312672,"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."}}