{"id":"W7099251704","doi":"","title":"Cross-sectional Weighting : Combining Two or More Panels, Statistics Canada. Catalogue 75F0002MIE-00006","year":2000,"lang":"en","type":"article","venue":"","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weighting; Summary statistics; Variety (cybernetics); Microform; Product (mathematics); Document retrieval; Information system","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":[0.007329263,0.0006953852,0.0009555098,0.006245123,0.00084,0.001748487,0.001693831,0.0007135281,0.1169257],"category_scores_gemma":[0.02546718,0.001240545,0.001058622,0.02028273,0.0005074186,0.001305607,0.001486085,0.000961288,0.03075511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007957627,"about_ca_system_score_gemma":0.02768331,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8597161,"about_ca_topic_score_gemma":0.8832219,"domain_scores_codex":[0.995758,0.001081873,0.0003554893,0.0004577311,0.001835059,0.0005117875],"domain_scores_gemma":[0.9908099,0.001981079,0.0006052494,0.001512317,0.004644855,0.0004466443],"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":[0.00003246496,0.0000247616,0.01006559,0.000233307,0.00006089974,0.00001510635,0.00006194289,0.0005557772,0.00004065139,0.002614545,0.9207513,0.06554361],"study_design_scores_gemma":[0.0001724503,0.00005084153,0.1739521,0.0005208189,0.0001799455,0.00008838882,0.0004359049,0.002898613,0.0003822286,0.005694299,0.8155631,0.00006125271],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.003547453,0.00532302,0.02112773,0.002481631,0.0005061898,0.001589302,0.8921134,0.00257812,0.0707332],"genre_scores_gemma":[0.03660754,0.009277523,0.056953,0.00129012,0.0002929908,0.00339035,0.7162501,0.001686343,0.1742521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8597161,"threshold_uncertainty_score":0.3911555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06393381415859924,"score_gpt":0.3504324885149127,"score_spread":0.2864986743563134,"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."}}