{"id":"W2122172923","doi":"10.1177/1460458205058757","title":"Current trends in publicly available genetic databases","year":2005,"lang":"en","type":"article","venue":"Health Informatics Journal","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Institute of Population and Public Health; University of Ottawa","funders":"","keywords":"Disease; Genomics; DNA microarray; Computational biology; Data science; Proteomics; Standardization; Genome; Bioinformatics; Computer science; Biology; Medicine; Gene; Genetics; Gene expression","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":[0.001697097,0.0002110326,0.0002607353,0.0005166793,0.0002164725,0.0001503345,0.0004479779,0.0001023051,0.0004259069],"category_scores_gemma":[0.0002295147,0.0001779514,0.00009961642,0.0003443824,0.0001184991,0.00004651549,0.0002223205,0.0005580087,0.0002799542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000126435,"about_ca_system_score_gemma":0.0007177977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003472188,"about_ca_topic_score_gemma":0.0003392108,"domain_scores_codex":[0.9966624,0.00008245795,0.001556548,0.0001419764,0.0006441704,0.0009124462],"domain_scores_gemma":[0.9982498,0.00001679247,0.0004313154,0.0004233233,0.0001781613,0.0007006492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004197199,0.0001791463,0.006832045,0.0001791679,0.00001922738,0.000001990506,0.0004900531,0.000178662,0.00008266399,0.00002841252,0.2168299,0.7751367],"study_design_scores_gemma":[0.001152122,0.0003962729,0.005197651,0.0000804217,0.00000395018,0.0002137557,0.0002976025,0.006268138,0.0004695322,0.00001824791,0.9856732,0.000229085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8585601,0.03824499,0.05221503,0.01583429,0.003475093,0.001393093,0.0004290483,0.0001020933,0.02974623],"genre_scores_gemma":[0.6312469,0.1246175,0.2154408,0.01480988,0.006356515,0.00007817477,0.001884583,0.0001408123,0.005424871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7749076,"threshold_uncertainty_score":0.7256647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06195384598442413,"score_gpt":0.3689272070161935,"score_spread":0.3069733610317693,"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."}}