{"id":"W1989108037","doi":"10.1016/j.jmp.2005.11.004","title":"Modelling intransitive preferences: A random-effects approach","year":2005,"lang":"en","type":"article","venue":"Journal of Mathematical Psychology","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Science Council","keywords":"Transitive relation; Pairwise comparison; Consistency (knowledge bases); Preference; Replication (statistics); Computer science; Econometrics; Invariant (physics); Mathematics; Cognitive psychology; Mathematical economics; Artificial intelligence; Psychology; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.004710206,0.0001698962,0.000824027,0.0003598609,0.00006409714,0.0001350295,0.0008596214,0.0001818704,0.0004384192],"category_scores_gemma":[0.001462859,0.0001024022,0.0003309519,0.000269723,0.0001618248,0.0003634796,0.0000434516,0.0004627873,0.000512208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003564155,"about_ca_system_score_gemma":0.00004745199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.519044e-7,"about_ca_topic_score_gemma":3.522102e-7,"domain_scores_codex":[0.9967293,0.0003225589,0.001624522,0.0003094717,0.000747423,0.000266694],"domain_scores_gemma":[0.9952516,0.003070167,0.0007233606,0.0004001742,0.0003524025,0.0002023181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001030244,0.001943184,0.00005974899,0.00002352492,0.00009540482,0.00007030036,0.003320622,0.03522523,0.0002548924,0.006352542,0.009076109,0.9425482],"study_design_scores_gemma":[0.002357146,0.0003652807,0.00004384051,0.00007997231,0.00006064835,0.0007991445,0.0002892526,0.08170793,0.00007703901,0.9103122,0.003741342,0.0001662179],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4151272,0.00009647408,0.570213,0.0007044625,0.0002459052,0.00009712773,0.000001218696,0.000007857067,0.01350682],"genre_scores_gemma":[0.878472,0.00002549386,0.1205894,0.0005072958,0.0002742897,0.000003582634,2.081661e-7,0.00001172428,0.0001160056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.942382,"threshold_uncertainty_score":0.6583568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2219304081732033,"score_gpt":0.4461721564620321,"score_spread":0.2242417482888287,"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."}}