{"id":"W4240094840","doi":"10.1002/0470013192.bsa459","title":"Optimal Scaling","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scaling; Transformation (genetics); Variety (cybernetics); Column (typography); Computer science; Scale (ratio); Ordinal data; Matrix (chemical analysis); Key (lock); Algorithm; Data Matrix; Multidimensional scaling; Mathematics; Statistics; Artificial intelligence; Physics; Geometry","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":[],"category_scores_codex":[0.0002560763,0.0001820953,0.000295507,0.0003820531,0.00005387092,0.00002372323,0.0005227168,0.0001165404,0.002558471],"category_scores_gemma":[0.00008058346,0.0001836742,0.0000315588,0.0004922784,0.0005766488,0.00005516324,0.00009577541,0.0002169743,0.00005623111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008711056,"about_ca_system_score_gemma":0.0001519216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001715397,"about_ca_topic_score_gemma":0.0002117637,"domain_scores_codex":[0.9983943,0.00001732031,0.000418296,0.0003495011,0.0005307859,0.0002897609],"domain_scores_gemma":[0.9990656,0.0000908557,0.0002988713,0.0003687485,0.00007066273,0.0001052564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007836132,0.001555566,0.001527467,0.0001640821,0.000007039632,0.00004060954,0.001029499,0.00004788514,0.0005718106,0.4340815,0.4972236,0.06374317],"study_design_scores_gemma":[0.001591567,0.0002330567,0.009411598,0.00123797,0.0002941207,0.00003356776,0.0005812835,0.001159686,0.0005702416,0.0415754,0.9409418,0.002369779],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01015811,0.00009370961,0.03429562,0.00005061534,0.0004617233,0.0007251151,0.001579668,0.0001959392,0.9524395],"genre_scores_gemma":[0.003499574,0.0002327266,0.8251919,0.00001183834,0.0001439667,0.00004105975,0.00002964996,0.0001443613,0.170705],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7908962,"threshold_uncertainty_score":0.9983533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03822121135356509,"score_gpt":0.3766746153828547,"score_spread":0.3384534040292896,"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."}}