{"id":"W2611896270","doi":"10.23889/ijpds.v1i1.331","title":"Increasing research capacity with ICES Data & Analytic Services (DAS)","year":2017,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"","keywords":"Deliverable; Business; Health care; Reputation; Population; Data science; Computer science; Medicine; Political science; Environmental health; Engineering","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.01439178,0.0001179325,0.000196376,0.0004670289,0.008320022,0.0008730211,0.01013468,0.00007152469,0.0001302828],"category_scores_gemma":[0.003715018,0.00008811166,0.00002197995,0.0002106189,0.0003676732,0.009360131,0.003231847,0.0007344893,0.00007044187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006186723,"about_ca_system_score_gemma":0.001797813,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01445598,"about_ca_topic_score_gemma":0.02249316,"domain_scores_codex":[0.9959552,0.0002668459,0.0005960594,0.0006103286,0.001976956,0.0005946116],"domain_scores_gemma":[0.9934295,0.0007696378,0.000866187,0.002544721,0.002122174,0.0002677619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003052931,0.00004176918,0.9762381,0.00009819015,0.00004938932,0.000007843923,0.0004345764,0.00001750098,0.000230627,0.003559022,0.01069775,0.008319886],"study_design_scores_gemma":[0.0008290318,0.00003773259,0.7788778,0.0003596508,0.00002511509,0.00007513414,0.000411925,0.009024957,0.000003756798,0.003525537,0.2066629,0.0001665292],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627288,0.00005567575,0.002574813,0.02227711,0.004710782,0.000643436,0.001953196,0.00004474165,0.005011423],"genre_scores_gemma":[0.9747535,0.0001396357,0.01959676,0.001742509,0.001664417,0.00001022748,0.001508083,0.00001617374,0.000568713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1973604,"threshold_uncertainty_score":0.9953438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5874081624918781,"score_gpt":0.6525756916735266,"score_spread":0.06516752918164848,"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."}}