{"id":"W6957553572","doi":"10.6068/dp14ba868a17272","title":"Trend 2000 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Household, Family and Personal Income | Country: Canada | Table: Family characteristics, by family type, age of older adult, and family income | Variable: 25 to 34 years, Lone-parent families, $60,000 and over | Units: #, 2000-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-119.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personal income; Census; Socioeconomic status; Demographic statistics; Economic statistics; Official statistics; Family income; Population; Household income; Summary statistics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001406961,0.001063682,0.001886412,0.0002688252,0.0002800049,0.0005821028,0.00170684,0.0004770261,0.001550482],"category_scores_gemma":[0.0001666907,0.001029727,5.259485e-7,0.0002925201,0.0007739048,0.0004536743,0.002010442,0.0006968558,0.00003059146],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003285414,"about_ca_system_score_gemma":0.006698564,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9983329,"about_ca_topic_score_gemma":0.9491507,"domain_scores_codex":[0.9930923,0.0006600512,0.001366957,0.002053394,0.001650971,0.001176329],"domain_scores_gemma":[0.9949833,0.0007450084,0.001014302,0.001993058,0.0001156224,0.001148665],"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":[0.0001473009,0.00004914649,0.001096816,0.0008991554,0.0001352051,0.0003922617,0.0000241181,0.000005296185,0.001285341,0.00009413008,0.9958056,0.00006563198],"study_design_scores_gemma":[0.0009408825,0.0001880869,0.009060896,0.0001617304,0.0004385045,0.0001217352,0.0003125661,0.0008852708,1.455781e-7,3.936018e-7,0.9867517,0.001138126],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004719303,0.005607798,0.000008974484,0.000003281746,0.001320595,0.0008384185,0.987001,0.00008133457,0.000419275],"genre_scores_gemma":[0.001064842,0.004676971,0.0007663316,0.0006510303,0.0001568275,0.0000144875,0.9908678,0.0002474743,0.001554264],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04918214,"threshold_uncertainty_score":0.9993622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018189596481584,"score_gpt":0.2468726670496907,"score_spread":0.2266907710848749,"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."}}