{"id":"W4401516883","doi":"10.1002/etc.5954","title":"The DIKW of transcriptomics in ecotoxicology: extracting information, knowledge, and wisdom from big data","year":2024,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ecotoxicology; Field (mathematics); Transcriptome; Salient; Data science; Big data; Biology; Toxicogenomics; Government (linguistics); Computer science; Ecology; Data mining; Genetics; Gene; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03591423,0.001477842,0.002418299,0.01066791,0.003623386,0.01878526,0.003738973,0.006308521,0.002512216],"category_scores_gemma":[0.06845532,0.0009780616,0.001479773,0.01140161,0.02335849,0.03642978,0.008873393,0.0122769,0.001649839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004740522,"about_ca_system_score_gemma":0.006406692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002221175,"about_ca_topic_score_gemma":0.002962968,"domain_scores_codex":[0.9825444,0.0093266,0.001060606,0.001770413,0.004901025,0.000397001],"domain_scores_gemma":[0.8719694,0.1083148,0.003220633,0.009180822,0.005317538,0.001996901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002088332,0.0001656701,0.01097519,0.007522278,0.0003791672,0.0007403393,0.01193635,0.003900687,0.003560534,0.4893595,0.09820232,0.3730491],"study_design_scores_gemma":[0.00001492898,0.00002972754,0.002283493,0.00282763,0.00003298975,0.0001926101,0.004740577,0.003133932,0.000732768,0.8657791,0.1201346,0.00009765931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01353327,0.1775961,0.3047422,0.4714641,0.008870859,0.0003810799,0.003676887,0.0007585598,0.01897694],"genre_scores_gemma":[0.1916492,0.2377803,0.4171768,0.108476,0.03085294,0.001382027,0.005374114,0.0008645566,0.006444104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03591423,"threshold_uncertainty_score":0.1899348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486820207943056,"score_gpt":0.2462052429650672,"score_spread":0.2313370408856366,"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."}}