{"id":"W6957911834","doi":"10.6068/dp14ba8ac7f3f73","title":"Trend 2003 - 2011. Statistics Canada. CANSIM: Population and Demography - Population Estimates and Projections | Country: Canada | Table: Neighbourhood income and demographics, taxfilers and dependents with after-tax income, by sex and age group | Variable: All age groups, Persons with after-tax income of $25,000 and over, Both sexes | Units: #, 2003-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-163.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Census; Residence; Socioeconomic status; Population statistics; Neighbourhood (mathematics); Economic statistics; Social statistics; Demographic 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"],"consensus_categories":[],"category_scores_codex":[0.0001847703,0.0005825185,0.0006829284,0.0002132111,0.0001394457,0.0002471061,0.0001944352,0.0002625156,0.0001335353],"category_scores_gemma":[0.00001354657,0.0005399223,2.093614e-7,0.0003000365,0.0002669077,0.0006595838,0.0001805827,0.000361682,9.727845e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009083896,"about_ca_system_score_gemma":0.0003365964,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940466,"about_ca_topic_score_gemma":0.9916489,"domain_scores_codex":[0.9978188,0.0001040015,0.000544097,0.0007255457,0.0004026049,0.0004049245],"domain_scores_gemma":[0.9985408,0.0001301435,0.0003598829,0.0005512608,0.00003405857,0.0003838385],"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":[0.00008913349,0.0000240963,0.1657769,0.0006890308,0.0003320997,0.0000459734,0.000008528419,0.0000251402,0.00000212241,0.00007512354,0.832763,0.0001687767],"study_design_scores_gemma":[0.00183277,0.0002589574,0.09181647,0.0001461812,0.0008372407,0.0001303845,0.0002329937,0.02114352,1.956497e-8,0.000001335988,0.8826784,0.0009217429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006144914,0.002383726,0.0001555043,7.484288e-7,0.0000968881,0.0007109941,0.9904295,0.00004324757,0.00003442744],"genre_scores_gemma":[0.03024904,0.0009985059,0.002630513,0.00002501184,0.0000295952,0.00003847525,0.9658268,0.0001274793,0.00007456275],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07396047,"threshold_uncertainty_score":0.9997053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008859685763722308,"score_gpt":0.2029453551300073,"score_spread":0.194085669366285,"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."}}