{"id":"W6976597528","doi":"10.6068/dp14baa3cf87a66","title":"Trend 2001 - 2004. Statistics Canada. CANSIM: Seniors - Housing and Living Arrangements | Country: Canada | Province: Newfoundland and Labrador | Table: Federal personnel engaged in science and technology and its components, by type of science, personnel category | Variable: Related scientific activities, Scientific and professional personnel, Natural sciences and engineering | Units: # Persons, 2001-2004. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-184.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Biographical and Historical Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Population; Population statistics; Statistics education; Socioeconomic status; Standard of living","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":[0.002238724,0.002581785,0.002896256,0.008057203,0.003398519,0.004853263,0.005611366,0.001703016,0.08492462],"category_scores_gemma":[0.01861095,0.001876153,0.002355163,0.03674925,0.0006335105,0.002332559,0.002404222,0.003171929,0.04998295],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04882185,"about_ca_system_score_gemma":0.1138442,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936609,"about_ca_topic_score_gemma":0.9921023,"domain_scores_codex":[0.995896,0.0002675148,0.0004854078,0.0005525428,0.001868353,0.0009302697],"domain_scores_gemma":[0.9649053,0.001214775,0.001174231,0.001077513,0.02989268,0.001735535],"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":[0.00002202799,0.000006184071,0.001163637,0.0002239951,0.0000214421,0.000006036815,0.00002247096,0.00008681824,0.00000674366,0.0002733346,0.996881,0.00128624],"study_design_scores_gemma":[0.0002051369,0.00001535273,0.03451362,0.00103859,0.00008855508,0.000031265,0.000583755,0.0004228959,0.0001647248,0.0006083911,0.9622315,0.00009626424],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005577291,0.00004277253,0.00002073108,0.0001009725,0.00002712163,0.00001408077,0.9990637,0.00004417646,0.0006307216],"genre_scores_gemma":[0.0007510071,0.00022777,0.0003129228,0.0001490975,0.00001960041,0.0001477403,0.9946578,0.00008154737,0.003652541],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9511781,"threshold_uncertainty_score":0.354229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02468322642817513,"score_gpt":0.2196369057602881,"score_spread":0.1949536793321129,"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."}}