{"id":"W3123880665","doi":"","title":"Pricing and Signaling with Frictions","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Microeconomics; Quality (philosophy); Inefficiency; Economics; Private information retrieval; Bargaining power; Matching (statistics); Differential (mechanical device); Ex-ante; Investment (military); Computer science","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":[0.005740539,0.0001755705,0.0003278308,0.000587175,0.0004613077,0.0003420711,0.0006146884,0.000217161,0.0002734222],"category_scores_gemma":[0.000983785,0.0001438383,0.00006735194,0.0003364575,0.0003831063,0.0001994722,0.000760616,0.001169538,0.00004680892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000212694,"about_ca_system_score_gemma":0.0002331616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002283221,"about_ca_topic_score_gemma":0.00005856628,"domain_scores_codex":[0.9973987,0.0003392937,0.0005794667,0.0007888167,0.0004373145,0.0004564416],"domain_scores_gemma":[0.9956458,0.002800109,0.0002426205,0.0009518212,0.0001473717,0.0002122505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001116392,0.0002384801,0.0300861,0.00004410597,0.00009903688,0.000007095575,0.00303415,0.03847528,0.0005050951,0.0283958,0.0001063721,0.8988969],"study_design_scores_gemma":[0.001639886,0.0002463182,0.05258445,0.0005310233,0.00007353855,0.0001763606,0.03215987,0.02975869,0.002230004,0.4369647,0.4413886,0.00224655],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8528346,0.0001661435,0.001415718,0.0006625595,0.0002576373,0.0007124904,0.00003121245,0.00005317155,0.1438665],"genre_scores_gemma":[0.9938455,0.001026691,0.001965706,0.00003923199,0.0002347612,0.000201412,0.000007307352,0.00002496098,0.002654493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8966503,"threshold_uncertainty_score":0.5865554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1163520220310025,"score_gpt":0.4034387369792307,"score_spread":0.2870867149482282,"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."}}