{"id":"W3187314870","doi":"10.3390/info12080317","title":"Design of Generalized Search Interfaces for Health Informatics","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ontology; Health informatics; Vocabulary; Interface (matter); Workflow; Domain (mathematical analysis); Controlled vocabulary; Information retrieval; Informatics; User interface; Set (abstract data type); Plug-in; Human–computer interaction; Data science; World Wide Web; Database; Public health; Engineering; Medicine; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003068122,0.00004785843,0.00009031065,0.00002815308,0.0000347441,0.0000151658,0.00006975239,0.00007075359,0.000006233957],"category_scores_gemma":[0.0001546831,0.00004178617,0.0000269481,0.00005453004,0.00003636873,0.000009534132,0.00003938054,0.00002843168,0.000004007361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000778145,"about_ca_system_score_gemma":0.0001725614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004523046,"about_ca_topic_score_gemma":0.000001493445,"domain_scores_codex":[0.9994213,0.00002878497,0.0003247919,0.00003360726,0.00008182607,0.000109677],"domain_scores_gemma":[0.999585,0.00001751951,0.0001124715,0.0001100401,0.0001453642,0.00002964698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004513915,0.00008246585,0.0002604368,0.0009946546,0.0001545984,2.371392e-7,0.007927652,0.007209221,0.08445412,0.0007327934,0.05248068,0.8452517],"study_design_scores_gemma":[0.001464836,0.0008343984,0.0002589674,0.00005049532,0.000007699065,0.00001483555,0.002343174,0.01543926,0.7619154,0.0001400207,0.2173703,0.0001605855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151414,0.0002854081,0.8836128,0.0004706471,0.00009734618,0.0001526751,0.0000253933,0.00001159685,0.0002026962],"genre_scores_gemma":[0.6958046,0.0005303941,0.3006712,0.001840011,0.00006778801,0.00003643845,0.0008418192,0.000006952919,0.0002007916],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8450912,"threshold_uncertainty_score":0.1703991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04229474773318576,"score_gpt":0.3360776097010882,"score_spread":0.2937828619679024,"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."}}