{"id":"W3125761330","doi":"","title":"Von Neumann-Morgenstern Stable Sets in Matching Problems","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Core (optical fiber); Combinatorics; Matching (statistics); Mathematics; Set (abstract data type); Independent set; Lattice (music); Characterization (materials science); Discrete mathematics; Computer science; Physics; Graph","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007392899,0.0005090475,0.00135402,0.001517353,0.0001643431,0.0003978705,0.001115165,0.000739424,0.000156071],"category_scores_gemma":[0.0002299893,0.0006717227,0.000257214,0.0002777434,0.0001852267,0.0002497929,0.00111602,0.002539587,0.0002353936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001538041,"about_ca_system_score_gemma":0.0001837473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004194742,"about_ca_topic_score_gemma":0.003259941,"domain_scores_codex":[0.9943113,0.0002967981,0.002172767,0.001662154,0.0001098574,0.001447141],"domain_scores_gemma":[0.9972668,0.0003715266,0.0007632776,0.001373629,0.00005205035,0.0001727749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001703144,0.0009585393,0.5584058,0.002876287,0.0002362229,0.0001713139,0.005075405,0.3458269,0.00008338725,0.05161012,0.0002899162,0.03429576],"study_design_scores_gemma":[0.004761624,0.0003552317,0.1543849,0.003986792,0.00001505408,0.00006080488,0.001747975,0.09676186,0.000122941,0.6213537,0.1117347,0.004714421],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7929738,0.0007321247,0.00002083034,0.0001661566,0.001029528,0.001076541,0.0002463764,0.00006209742,0.2036926],"genre_scores_gemma":[0.9885365,0.001476957,0.0002791489,0.00004896764,0.000258584,0.0003215082,0.0001401893,0.0001459245,0.008792168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5697436,"threshold_uncertainty_score":0.9997616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283309489618306,"score_gpt":0.2881627785131244,"score_spread":0.2353296836169413,"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."}}