{"id":"W6976736754","doi":"10.6068/dp14ba896259925","title":"Trend 1996 - 2011. Statistics Canada. CANSIM: Education, Training and Learning - Adult Education and Training | Country: Canada | Table: Registered apprenticeship training, registrations, by age groups, sex and major trade groups | Variable: 50 years and over, User support technicians, Total registration status, Males | Units: #, 1996-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-067.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics education; Census; Apprenticeship; Adult education; Descriptive statistics; Economic statistics; Official statistics; Training (meteorology)","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006157054,0.0006688939,0.0007661311,0.0001368802,0.0005105149,0.001771909,0.0005349878,0.0003086299,0.0013415],"category_scores_gemma":[0.0001068389,0.0007208445,4.739698e-7,0.00005570478,0.0006217823,0.001093624,0.0001724758,0.0008679085,0.000001862297],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416913,"about_ca_system_score_gemma":0.008450507,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932491,"about_ca_topic_score_gemma":0.9966187,"domain_scores_codex":[0.9962962,0.0002878938,0.0008837825,0.001147368,0.0007022276,0.0006825121],"domain_scores_gemma":[0.9972026,0.0003912874,0.0008520682,0.000958836,0.00005683459,0.0005383365],"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":[0.00004047505,0.00006670546,0.00004265081,0.0005800712,0.0001410637,0.00005416881,0.0007078688,3.467587e-7,0.00000123867,0.01725559,0.9783509,0.002758864],"study_design_scores_gemma":[0.0005812991,0.0001488662,0.00004881823,0.0001109795,0.0003479004,0.0002586322,0.03326136,0.00009766525,3.774384e-9,0.000008927012,0.9643291,0.0008064928],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005133922,0.00501219,0.000003382665,0.00001109335,0.0005216788,0.0005506699,0.9798096,0.00007880855,0.01396125],"genre_scores_gemma":[0.001109125,0.001145268,0.0002005182,0.0003176386,0.0004434904,0.0000359624,0.9608735,0.0001786332,0.03569583],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03255349,"threshold_uncertainty_score":0.9995714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04194951296657233,"score_gpt":0.2455917022931229,"score_spread":0.2036421893265506,"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."}}