{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.002012511,0.0004592552,0.0006488637,0.0008950274,0.003262181,0.001333057,0.0006359714,0.0001901288,0.0001984765],"category_scores_gemma":[0.0002704872,0.0003908316,2.703743e-7,0.001280243,0.007589753,0.0008057515,0.0006501803,0.0008700196,3.073472e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261692,"about_ca_system_score_gemma":0.005781263,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9833621,"about_ca_topic_score_gemma":0.9850467,"domain_scores_codex":[0.9960673,0.0001675732,0.0004188854,0.001410546,0.001121923,0.0008137769],"domain_scores_gemma":[0.9982893,0.0003850803,0.0003131706,0.0004121917,0.0001481243,0.0004520788],"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":[0.00002283544,0.00005088699,0.0002745681,0.0004372055,0.00006076758,0.000036906,0.0004252052,0.000001065961,0.0003508442,0.0006146826,0.9975908,0.0001341638],"study_design_scores_gemma":[0.0004640887,0.00006722885,0.00002243665,0.0001332706,0.0001477801,0.00006693219,0.0132838,0.01373811,1.028235e-7,5.110902e-7,0.9715672,0.000508502],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008957405,0.02000888,3.236839e-7,0.0000130577,0.0007383088,0.0003784417,0.9696747,0.0000291447,0.0001997269],"genre_scores_gemma":[0.0426822,0.000537779,0.000105189,0.00003739383,0.00006471588,0.000006010484,0.9526535,0.00005498275,0.003858243],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0337248,"threshold_uncertainty_score":0.999855,"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."}}