{"id":"W2891267781","doi":"10.23889/ijpds.v3i4.922","title":"Mapping Clinical Contents onto Longitudinal Depictions of Cross-Continuum Service Events in Island Health: Clinical Context Coding Scheme","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Healthcare Systems and Technology","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Island Health; University of British Columbia","funders":"","keywords":"Computer science; Coding (social sciences); Software deployment; Data science; Image stitching; Service (business); Context (archaeology); Software engineering; Artificial intelligence; Business; Geography; Marketing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.006525314,0.0001156347,0.0003368566,0.0005958105,0.0004189617,0.0002314606,0.001543292,0.0001011745,0.00003658667],"category_scores_gemma":[0.003030893,0.0001077869,0.00008136535,0.0005884374,0.0002531879,0.003534345,0.0005263496,0.0002910043,0.00002636732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001251926,"about_ca_system_score_gemma":0.0001823573,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008134077,"about_ca_topic_score_gemma":0.03604249,"domain_scores_codex":[0.9967063,0.00003998281,0.001844761,0.000469635,0.0006065425,0.0003327908],"domain_scores_gemma":[0.9959493,0.0001415777,0.001371478,0.0003614264,0.00212581,0.00005043843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006776951,0.00009151504,0.9775788,0.00002621572,0.00002316321,0.00000209887,0.00002232201,0.00000223423,0.00003831187,0.006041376,0.0007138855,0.01539229],"study_design_scores_gemma":[0.001408228,0.00004384742,0.9552908,0.0003167534,0.000003763698,0.00003036857,0.0001488153,0.0167831,0.00000306776,0.001437683,0.02443073,0.0001028353],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751522,0.00003373314,0.01109419,0.004470811,0.008664256,0.0003431914,0.00007077051,0.00002795806,0.0001428708],"genre_scores_gemma":[0.9946017,0.00001964378,0.001236831,0.001420229,0.002552669,0.000005338273,0.0001125512,0.000009846088,0.00004119046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02790841,"threshold_uncertainty_score":0.9984708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.326119958882425,"score_gpt":0.5149831215027734,"score_spread":0.1888631626203484,"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."}}