{"id":"W2238903494","doi":"10.1017/s1365100512000466","title":"ENDOGENOUSLY SEGMENTED ASSET MARKET IN AN INVENTORY-THEORETIC MODEL OF MONEY DEMAND","year":2012,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Economics; Monetary economics; Market liquidity; Inflation (cosmology); Endogenous money; Monetary policy; Asset (computer security); Market segmentation; Econometrics; Money market; Velocity of money; 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":[0.001464916,0.0002952872,0.0007533895,0.0003628887,0.00006423829,0.00004916589,0.0004611243,0.0001900853,0.0005907462],"category_scores_gemma":[0.00003043155,0.0003888794,0.0001606318,0.00009567339,0.0001626097,0.0007237395,0.0001483111,0.0001992152,0.0001251184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006173849,"about_ca_system_score_gemma":0.00004337481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002450549,"about_ca_topic_score_gemma":0.0004617831,"domain_scores_codex":[0.9974977,0.00004104293,0.00124378,0.0004801447,0.00002372812,0.0007136207],"domain_scores_gemma":[0.9984366,0.00005049274,0.0005953961,0.0006647877,0.00001992619,0.0002327469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007464477,0.0002542601,0.2323947,0.0000573832,0.00005608594,8.689192e-7,0.001075421,0.01827477,0.00004400942,0.7473567,0.0001204005,0.0002907699],"study_design_scores_gemma":[0.0009627952,0.00006077404,0.003008472,0.0000124839,0.00001137281,0.000006062997,0.0002624577,0.8542842,0.00006193798,0.1406351,0.0002820977,0.0004123159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563458,0.0005845636,0.01094523,0.00007913627,0.0004377301,0.0003382163,0.0009275587,0.00003576155,0.03030603],"genre_scores_gemma":[0.9966664,0.0002220443,0.001596909,0.0001673399,0.00007341258,0.00004439133,0.0001120977,0.00007229032,0.001045089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8360094,"threshold_uncertainty_score":0.9998563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496656459018097,"score_gpt":0.2198725776329641,"score_spread":0.1949060130427832,"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."}}