{"id":"W2890154821","doi":"10.23889/ijpds.v3i4.797","title":"A Data Science Approach to Predictive Analytic Research and Knowledge Translation","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Ottawa Hospital; Bruyère; University of Ottawa","funders":"","keywords":"Computer science; Workflow; Documentation; Predictive analytics; Data pre-processing; Data mining; Machine learning; Data science; Software engineering; Database; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01343145,0.0001212569,0.0001088734,0.0006399532,0.001686593,0.0006096223,0.004662458,0.00003635546,0.00008454191],"category_scores_gemma":[0.002580692,0.0001121209,0.00001270737,0.001622105,0.002455684,0.00803373,0.00286417,0.0002642974,0.0001076586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007033976,"about_ca_system_score_gemma":0.0002698887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002892015,"about_ca_topic_score_gemma":0.0002253521,"domain_scores_codex":[0.9953635,0.00008899027,0.0003943112,0.001384802,0.002222485,0.0005459145],"domain_scores_gemma":[0.9978266,0.0002174783,0.0001470969,0.001022573,0.0003541729,0.0004320295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000707535,0.001466374,0.2117474,0.00003607679,0.00009024986,0.00001070561,0.01062734,0.003114457,0.05081771,0.007409867,0.0114688,0.7025035],"study_design_scores_gemma":[0.0004798974,0.0002480115,0.3428121,0.00005254497,0.00001329426,0.00008634269,0.000426942,0.6270357,0.0003004431,0.002372452,0.02595647,0.0002158186],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6889368,0.0000574757,0.2855773,0.002478287,0.001804107,0.001610011,0.001213643,0.00004648403,0.01827588],"genre_scores_gemma":[0.9709758,0.00003080319,0.02810232,0.0001518307,0.0004233541,0.00001161735,0.0001951987,0.00001117428,0.00009788733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7022877,"threshold_uncertainty_score":0.999613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4005225638763397,"score_gpt":0.5098085123995284,"score_spread":0.1092859485231887,"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."}}