{"id":"W2098303558","doi":"10.1093/nar/gkm755","title":"CEBS Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data","year":2007,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Canada; National Institutes of Health","keywords":"Biology; Proteomics; Computational biology; Toxicity; DNA microarray; Bioinformatics; Data science; Genetics; Gene expression; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005097842,0.0001518357,0.0001881942,0.000107201,0.0001588648,0.0001951765,0.001684083,0.0002021299,0.00000123626],"category_scores_gemma":[0.001053842,0.0001096713,0.000005316233,0.0002619707,0.000303087,0.0000401112,0.003473614,0.0004090821,8.431102e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003484851,"about_ca_system_score_gemma":0.0001803762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007999495,"about_ca_topic_score_gemma":0.00005117113,"domain_scores_codex":[0.9970465,0.0006801716,0.0002784095,0.001278186,0.0003203979,0.0003963384],"domain_scores_gemma":[0.9968075,0.0001523435,0.00007029177,0.00269483,0.0001133943,0.0001616746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000346405,0.000213898,0.03248627,0.00004911913,0.00002753854,0.00001864614,0.00008611631,4.08288e-7,0.9618381,0.00001128891,0.001484367,0.003437849],"study_design_scores_gemma":[0.007591148,0.00482799,0.08055114,0.0005615024,0.00005843881,0.0004190734,0.01360127,0.02970604,0.8429938,0.00004632919,0.01827864,0.001364605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796538,0.0009848947,0.01771189,0.0001096104,0.00003801113,0.001337716,0.00003937459,0.00001318148,0.0001115656],"genre_scores_gemma":[0.9894145,0.0001037605,0.00964094,0.00002125285,0.0001340766,0.00004669811,0.0005850583,0.00001873348,0.00003494244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1188443,"threshold_uncertainty_score":0.4472265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1397727400866712,"score_gpt":0.374835628942579,"score_spread":0.2350628888559078,"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."}}