{"id":"W2129461932","doi":"","title":"U.S. and Canada City Areas","year":2008,"lang":"en","type":"article","venue":"","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Class (philosophy); Generalization; Set (abstract data type); Artificial intelligence; Computer science; Geography; Mathematics; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002305039,0.0009585306,0.000678897,0.00495289,0.001980883,0.002139634,0.001113028,0.0003896934,0.03849504],"category_scores_gemma":[0.002413682,0.0002787768,0.0004314258,0.01770083,0.0003912633,0.0008228414,0.0008058283,0.0008482893,0.01464787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009971926,"about_ca_system_score_gemma":0.01917913,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9752578,"about_ca_topic_score_gemma":0.9872177,"domain_scores_codex":[0.9991106,0.00003647138,0.00003713893,0.0001407553,0.000481811,0.0001931746],"domain_scores_gemma":[0.9965137,0.0000870173,0.0001671418,0.0001165372,0.002864494,0.0002512369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003613507,0.00001401051,0.01366881,0.00007765698,0.00001968172,0.00004226318,0.0001141516,0.000341338,0.00004640986,0.002592593,0.9704949,0.01255199],"study_design_scores_gemma":[0.00002495538,0.00001103654,0.1096802,0.000123746,0.00002445126,0.0000724744,0.001079064,0.0006271685,0.000255381,0.0005938368,0.8874663,0.00004133517],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.007034391,0.0004972767,0.0003252125,0.0004971838,0.0001048455,0.00006036164,0.9298295,0.0002745949,0.06137654],"genre_scores_gemma":[0.05594672,0.001492971,0.001636011,0.0004115667,0.00005781418,0.0001633294,0.8766239,0.0001557661,0.06351195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03849504,"threshold_uncertainty_score":0.1287787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169648187119814,"score_gpt":0.1597179298658887,"score_spread":0.1280214479946906,"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."}}