{"id":"W3013218212","doi":"10.23889/ijpds.v4i2.1133","title":"Population Data BC: Supporting population data science in British Columbia.","year":2019,"lang":"en","type":"article","venue":"PubMed","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; The Quebec Population Health Research Network","funders":"","keywords":"Data access; Computer science; Variety (cybernetics); Data science; Data governance; Data quality; Identifier; Population; Linkage (software); Linked data; Record linkage; Data management; Process (computing); Database; World Wide Web; Business; Service (business)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02203051,0.00006731795,0.0002229181,0.0002044843,0.000177295,0.002938211,0.004912214,0.00004153856,0.000331026],"category_scores_gemma":[0.008346939,0.00009345952,0.0000160141,0.001529182,0.00007277556,0.007736121,0.004169266,0.0001042054,0.0001666357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008645344,"about_ca_system_score_gemma":0.00003882686,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1580458,"about_ca_topic_score_gemma":0.3494828,"domain_scores_codex":[0.99532,0.0001654118,0.0008642237,0.001305846,0.001837328,0.0005072337],"domain_scores_gemma":[0.9954697,0.0002469458,0.0003771423,0.003710012,0.00008035678,0.0001158583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001561041,0.00002396675,0.5826125,0.000004873101,0.000001132154,0.000002560125,0.00001196967,0.00001718329,7.845744e-7,0.0001183565,0.007094022,0.4101111],"study_design_scores_gemma":[0.0002160199,0.000003293998,0.9708968,0.000009030001,0.000004715321,0.000001768319,0.0002561405,0.008206179,3.553785e-7,0.003394604,0.01688833,0.0001228075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941729,0.00001577444,0.0003001573,0.0004656349,0.0009856627,0.001019101,0.0004301241,0.00004510407,0.002565586],"genre_scores_gemma":[0.9941273,0.000006685902,0.0005881706,0.0002464635,0.00005898546,0.00004047568,0.002311794,0.000007030857,0.00261304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4099883,"threshold_uncertainty_score":0.9992669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2812092852371275,"score_gpt":0.4112615858905286,"score_spread":0.1300523006534012,"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."}}