{"id":"W6939645956","doi":"10.6068/dp14bace4969f67","title":"Trend 1991 - 2050. United States Census Bureau. Components of Population Change - International: Net Migration Rate | Country: Canada, 1991-2050. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 001-036-004.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Net migration rate; Population; Population statistics; Population growth; Demographic analysis; Emigration; Immigration","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006053752,0.0005376307,0.0006882034,0.0004340726,0.0001014324,0.0002160739,0.005313913,0.0004102067,0.0001495062],"category_scores_gemma":[0.0000454832,0.0005159111,8.346235e-7,0.0001665714,0.0001943343,0.0005089333,0.00154756,0.000494211,0.000141321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002208134,"about_ca_system_score_gemma":0.0000255696,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9985055,"about_ca_topic_score_gemma":0.9148001,"domain_scores_codex":[0.9962618,0.0003559229,0.0007853026,0.001109997,0.001016744,0.0004702073],"domain_scores_gemma":[0.9950362,0.0003296522,0.001052119,0.003342281,0.00004993805,0.000189848],"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":[0.00003308123,0.00008051063,0.00000238581,0.0001235981,0.0001185697,0.0001008068,0.000008528046,0.00005933033,0.000004857468,0.0008779817,0.9979826,0.0006077072],"study_design_scores_gemma":[0.0004263806,0.00007346074,0.00003697792,0.00005273975,0.00006156784,0.0000394103,0.00001947109,0.06967726,4.383525e-9,0.000004455341,0.9291192,0.0004890378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000005376749,0.001140019,0.00000276479,0.00006908939,0.001511533,0.0004555967,0.9957045,0.0004800004,0.0006311715],"genre_scores_gemma":[0.0001535679,0.0004899816,0.001095806,0.0002213881,0.0002008702,0.0000110366,0.997092,0.00009940299,0.0006359397],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08370537,"threshold_uncertainty_score":0.9997293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07044171334059525,"score_gpt":0.2871164638107968,"score_spread":0.2166747504702015,"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."}}