{"id":"W6976736109","doi":"10.6068/dp14ba893af6263","title":"Trend 2004 - 2009. Statistics Canada. CANSIM: Education, Training and Learning - Fields of Study | Country: Canada | Table: College enrolments, by registration status, program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex | Variable: Physical and life sciences and technologies, Other program level, Males, Part-time student | Units: #, 2004-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-071.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics education; Census; Economic statistics; Statistical analysis; Publication; Socioeconomic status; Official statistics; Social 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002792902,0.002605718,0.003104058,0.01036562,0.003539838,0.005049294,0.005752905,0.001627485,0.1018658],"category_scores_gemma":[0.02503689,0.001921004,0.00236831,0.04649055,0.0006954117,0.002512082,0.002498373,0.003167609,0.05524296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06495235,"about_ca_system_score_gemma":0.177279,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944706,"about_ca_topic_score_gemma":0.9920152,"domain_scores_codex":[0.9938268,0.0003957445,0.0007904145,0.0007446339,0.002951287,0.001291073],"domain_scores_gemma":[0.9479268,0.002001509,0.001543807,0.00134755,0.04490247,0.00227784],"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":[0.00002594872,0.000006242867,0.0009371917,0.0003012509,0.00002175229,0.000005711756,0.000020111,0.0001088459,0.000009750239,0.0003489934,0.996581,0.001633109],"study_design_scores_gemma":[0.0001729615,0.00001329427,0.02482694,0.001069534,0.00008409211,0.00002519525,0.0004480813,0.0004150804,0.0001672425,0.0006644115,0.9720238,0.00008932559],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004446987,0.00004809949,0.0000233572,0.0001259882,0.00003149653,0.00001656702,0.9988334,0.0000547765,0.0008219245],"genre_scores_gemma":[0.0008687404,0.0003397098,0.0004794084,0.0002094557,0.00002306276,0.0001707107,0.993222,0.0001291521,0.004557712],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1018658,"threshold_uncertainty_score":0.4712645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06947827061146403,"score_gpt":0.2617771254075566,"score_spread":0.1922988547960926,"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."}}