{"id":"W2407872090","doi":"10.5281/zenodo.3781677","title":"A New Initiative: Access to the Statistics Canada's Public Use Microdata Files Collection","year":2011,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Microdata (statistics); Public use; Computer science; Data collection; Data science; Statistics; Political science; Census; Sociology; Mathematics; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001499835,0.0001167877,0.0001236667,0.0003348088,0.00230574,0.003256277,0.002180584,0.00003682706,0.01583494],"category_scores_gemma":[0.006592562,0.00008952896,0.00002612134,0.002080354,0.00006770897,0.000746144,0.001074459,0.000175679,0.001464464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080479,"about_ca_system_score_gemma":0.00006287263,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0260349,"about_ca_topic_score_gemma":0.01258371,"domain_scores_codex":[0.9974036,0.0005041921,0.0003886618,0.0004379002,0.0009561909,0.0003094902],"domain_scores_gemma":[0.9971418,0.0002021633,0.0001737407,0.0006553272,0.001559032,0.0002679211],"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":[0.00003574456,0.00002566179,0.00005827681,0.000002082495,0.0000152997,0.000004040983,0.001227675,0.00005380021,0.00004954822,0.001768148,0.9063843,0.09037539],"study_design_scores_gemma":[0.0001732867,0.000084861,0.01246627,0.00000778185,0.000009056823,0.00002318664,0.0009231037,0.001495882,0.00006616337,0.001305881,0.9833186,0.0001259027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03556729,0.00006364497,0.885,0.01051762,0.0007892752,0.001304886,0.004334438,0.0006200228,0.06180283],"genre_scores_gemma":[0.9841176,0.0000858961,0.006460312,0.0020764,0.0001561866,1.874088e-7,0.001871516,0.0006766531,0.004555197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9485503,"threshold_uncertainty_score":0.999313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3580726062186095,"score_gpt":0.344023153994886,"score_spread":0.01404945222372356,"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."}}