{"id":"W2033193801","doi":"10.1016/s0734-9750(02)00025-3","title":"DNA microarrays and toxicogenomics: applications for ecotoxicology?","year":2002,"lang":"en","type":"article","venue":"Biotechnology Advances","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Environment and Climate Change Canada","funders":"","keywords":"Toxicogenomics; DNA microarray; Biology; Gene expression; Computational biology; Gene expression profiling; Gene; Microarray; Genetics; Regulation of gene expression; Toxicology","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":[0.00004634284,0.0001005749,0.00009403272,0.00006334403,0.0001278566,0.00000846158,0.0001589309,0.0003056598,0.00001833052],"category_scores_gemma":[0.00003849549,0.00009904133,0.0000338687,0.00007095444,0.0001974966,0.000004055371,0.00005182383,0.00005501721,0.00001037656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009691058,"about_ca_system_score_gemma":0.000008860951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.880146e-7,"about_ca_topic_score_gemma":0.000008587711,"domain_scores_codex":[0.9993107,0.00001102162,0.0001252758,0.0003633958,0.00002468728,0.0001648702],"domain_scores_gemma":[0.9995716,0.00001299511,0.0000725996,0.0002790439,0.00002718197,0.00003655533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001328935,0.00003301982,0.0001186733,0.00001079909,0.00001033369,7.602989e-8,0.000006547394,0.000002191056,0.9169191,0.001499101,0.002092917,0.07929394],"study_design_scores_gemma":[0.0001908658,0.00008577768,0.00003710586,0.000001255674,0.000004449932,0.000006700714,0.00004255725,0.00002268555,0.4626709,0.0005195235,0.5363463,0.00007192285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6734428,0.09336819,0.2113918,0.01476369,0.0006385733,0.002920218,0.0001717292,0.0003022712,0.00300074],"genre_scores_gemma":[0.9718186,0.01278629,0.01224692,0.0006503123,0.000224987,0.001011839,0.0000493284,0.00002304408,0.001188698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5342534,"threshold_uncertainty_score":0.4038788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172366791021049,"score_gpt":0.2438940347432041,"score_spread":0.2321703668329936,"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."}}