{"id":"W4389920688","doi":"10.26509/frbc-ddb-20231219","title":"Urban and Regional Migration Estimates, Third Quarter 2023 Update","year":2023,"lang":"en","type":"report","venue":"Cleveland Fed District data briefs","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Geography; Pandemic; Track (disk drive); Regional science; Coronavirus disease 2019 (COVID-19); Computer science; Archaeology","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":[0.003401856,0.001766215,0.0008476349,0.004106866,0.0008579199,0.002656331,0.002335963,0.001908185,0.03572444],"category_scores_gemma":[0.009233951,0.0009245992,0.001258995,0.008080488,0.0002639219,0.00182716,0.001769877,0.002793072,0.03644314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004142823,"about_ca_system_score_gemma":0.009718815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3149426,"about_ca_topic_score_gemma":0.2182535,"domain_scores_codex":[0.997849,0.0001855063,0.0002587648,0.000150703,0.001255063,0.00030107],"domain_scores_gemma":[0.9931236,0.0004991427,0.0006318425,0.0003292088,0.004838509,0.0005778082],"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":[0.00002627806,0.00001522262,0.0008998394,0.00009029426,0.000004359678,0.00001021513,0.00001240394,0.0001266845,0.00001729174,0.000181636,0.9920293,0.006586431],"study_design_scores_gemma":[0.00004382779,0.00002101709,0.01781526,0.000216436,0.00001583346,0.00002785332,0.0001010245,0.0002263691,0.0001692189,0.0002731359,0.9810657,0.00002436717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001371663,0.001771281,0.0007086254,0.005333831,0.005337398,0.0002241989,0.9629708,0.0007078141,0.02157436],"genre_scores_gemma":[0.01191052,0.005809681,0.004424035,0.004142355,0.001769487,0.002458984,0.8785552,0.0006824511,0.09024722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3149426,"threshold_uncertainty_score":0.6262189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3004446489199364,"score_gpt":0.3831040696611541,"score_spread":0.08265942074121768,"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."}}