{"id":"W6901756218","doi":"10.6068/dp1665eb0d60d9","title":"TREND: Statistics Canada. CANSIM: Income and Expenditure Accounts | Table: 384-0035 | Variable: Net migration (persons x 1,000)-$CAD, 1961 - 1980. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 075-001-062","year":2018,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; National accounts; Summary statistics; Official statistics; Census; Measures of national income and output; National Income and Product Accounts; Economic data; Descriptive 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":[0.001794029,0.002278065,0.002207967,0.01197544,0.003141195,0.004980308,0.004332851,0.001272777,0.1973879],"category_scores_gemma":[0.01612888,0.001532372,0.001635343,0.05687683,0.000695901,0.002988154,0.002020359,0.00256408,0.1215504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04653067,"about_ca_system_score_gemma":0.1213905,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9912636,"about_ca_topic_score_gemma":0.9880165,"domain_scores_codex":[0.9958465,0.0002354141,0.0004018841,0.0005517917,0.002070143,0.0008943704],"domain_scores_gemma":[0.9658647,0.001326614,0.001144641,0.00129475,0.02889941,0.001469948],"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":[0.000009923955,0.000002659363,0.0004551967,0.0001218287,0.000007859843,0.00000402409,0.00001254093,0.00005745416,0.000004568057,0.0002937533,0.9975191,0.001511093],"study_design_scores_gemma":[0.00004799561,0.000004306685,0.008916969,0.0003812006,0.0000237226,0.0000145515,0.0002357253,0.0002159901,0.00007441232,0.0004201998,0.9896182,0.00004663061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003368396,0.00003873829,0.00002657882,0.0001205193,0.00002792168,0.00001545564,0.997767,0.0001041658,0.001866017],"genre_scores_gemma":[0.0008148273,0.0003428633,0.0003905299,0.0001506137,0.00002326688,0.0001413619,0.9888344,0.0002282409,0.009073863],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1973879,"threshold_uncertainty_score":0.6603285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788636591375973,"score_gpt":0.2499663833312566,"score_spread":0.2320800174174969,"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."}}