{"id":"W2889909038","doi":"10.23889/ijpds.v3i4.757","title":"Exploring Alternative Designs using ‘Big’ Administrative Data","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Programmer; Computer science; Missing data; Population; Set (abstract data type); Covariate; Macro; Sample (material); Data science; Medicine; Environmental health; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1587251,0.001800544,0.001817285,0.002657932,0.001669751,0.005066597,0.003767182,0.001869842,0.01676818],"category_scores_gemma":[0.2803746,0.001524363,0.005482287,0.002906235,0.003598867,0.004303909,0.004987823,0.003305428,0.0009255907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002897507,"about_ca_system_score_gemma":0.004372534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002865657,"about_ca_topic_score_gemma":0.003353857,"domain_scores_codex":[0.7641382,0.2168593,0.004923113,0.006117689,0.006948506,0.001013177],"domain_scores_gemma":[0.3181946,0.6317593,0.01218749,0.02955376,0.007363515,0.0009413203],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002995964,0.001126651,0.0325481,0.006505774,0.004552498,0.0006184459,0.006980333,0.08965296,0.001821145,0.4731703,0.01315349,0.3668744],"study_design_scores_gemma":[0.001076032,0.002207356,0.01124543,0.001407024,0.001067306,0.0002590378,0.002247112,0.2154577,0.003635405,0.7160969,0.04500929,0.0002913921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03088516,0.0004603492,0.9599642,0.001423727,0.0002090437,0.001200679,0.001784641,0.0007758062,0.003296433],"genre_scores_gemma":[0.08090078,0.000163724,0.913832,0.0004016491,0.00007411284,0.00313622,0.0009171102,0.0001308462,0.0004435128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8412749,"threshold_uncertainty_score":0.8394285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9893349254163367,"score_gpt":0.7973638719700463,"score_spread":0.1919710534462904,"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."}}