{"id":"W7097576705","doi":"","title":"Evaluating search and matching models using experimental data”, mimeo","year":2005,"lang":"en","type":"article","venue":"","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Earnings; Randomized experiment; Measure (data warehouse); Test (biology); Quasi-experiment; Constant (computer programming); Corporation; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001928706,0.00009454046,0.0001987567,0.00008411742,0.0001488916,0.0001070194,0.0002101013,0.00004339245,0.0002472507],"category_scores_gemma":[0.00001819731,0.0001050582,0.00002182963,0.00006186547,0.00002919854,0.0006168938,0.000220782,0.00008878845,0.0001120802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004944119,"about_ca_system_score_gemma":0.000008597728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005424884,"about_ca_topic_score_gemma":0.00000836965,"domain_scores_codex":[0.9989775,0.00003213522,0.0003610657,0.0003765878,0.00003627327,0.0002163695],"domain_scores_gemma":[0.9994497,0.00004413987,0.00007537491,0.0003655723,0.000008648562,0.00005659913],"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":[0.00003266528,0.0001628183,0.003246643,0.00006806172,0.00008401882,0.000002311144,0.00865177,0.1155551,0.008222794,0.8585516,0.00008684138,0.005335446],"study_design_scores_gemma":[0.0002960791,0.00002241625,0.00004736863,0.00001739282,0.000002112188,0.00001436724,0.0007834554,0.9886163,0.0008041601,0.009096651,0.00014795,0.0001517022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966345,0.001828537,0.02102493,0.00007146428,0.00009423817,0.0001179357,0.00002662241,0.0000367914,0.01045448],"genre_scores_gemma":[0.9711706,0.000006027207,0.02781486,0.00009100643,0.0001689977,0.000002114864,0.000006017745,0.00001733181,0.0007230465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8730612,"threshold_uncertainty_score":0.428415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4532770094041289,"score_gpt":0.3997619628980919,"score_spread":0.053515046506037,"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."}}