{"id":"W2189604740","doi":"","title":"Matching Markets with Couples Revisited","year":2010,"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":"University of Toronto","funders":"","keywords":"Matching (statistics); Constant (computer programming); Class (philosophy); Incentive compatibility; Blossom algorithm; Stable marriage problem; Mathematics; Incentive; Computer science; Econometrics; Mathematical optimization; Algorithm; Economics; Statistics; Artificial intelligence; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007875049,0.00008435173,0.0001966789,0.00007344381,0.00007033518,0.00006705373,0.0001315179,0.00005300634,0.00172476],"category_scores_gemma":[0.00003244901,0.00007341537,0.000033543,0.0001020596,0.00002969605,0.0001069688,0.00001842533,0.0001510625,0.0008994629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006477366,"about_ca_system_score_gemma":0.000004011841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009341281,"about_ca_topic_score_gemma":0.00003867582,"domain_scores_codex":[0.9993669,0.000007658366,0.0002481999,0.0002040568,0.00001635326,0.0001567818],"domain_scores_gemma":[0.9994908,0.00005537124,0.0001354562,0.0002564269,0.00001331382,0.00004862338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001487031,0.00001631212,0.04075572,0.00002196063,0.00001679057,0.000002263642,0.0001567569,0.000003279196,0.0002659881,0.9582466,0.0002859196,0.0002135676],"study_design_scores_gemma":[0.002189347,0.0001871896,0.2094566,0.0001717473,0.00001713573,0.0001767407,0.0007204859,0.002594977,0.001113599,0.3963552,0.3854933,0.0015238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7974488,0.00009191579,0.002563094,0.0001336759,0.0001969108,0.00008242662,0.00001150242,0.00007266235,0.199399],"genre_scores_gemma":[0.991232,0.000006340823,0.001529322,0.0001462432,0.0001002286,0.00000515223,0.000003980262,0.00001559428,0.006961088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5618914,"threshold_uncertainty_score":0.9998785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400668201362244,"score_gpt":0.1962115494797363,"score_spread":0.1822048674661139,"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."}}