{"id":"W2117730418","doi":"10.1038/nprot.2006.283","title":"High-throughput screening of small molecules for bioactivity and target identification in Caenorhabditis elegans","year":2006,"lang":"en","type":"article","venue":"Nature Protocols","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":125,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Caenorhabditis elegans; Biology; Mutant; High-throughput screening; Small molecule; Phenotypic screening; Agar plate; Model organism; Phenotype; Chemical genetics; Identification (biology); Computational biology; Caenorhabditis; Genetic screen; Genetics; Bacteria; Gene; Botany","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":[0.0002489921,0.0001479864,0.0001834909,0.00006035094,0.00005321827,0.00003114769,0.000145958,0.0003539578,0.000002084174],"category_scores_gemma":[0.00007255925,0.0001542181,0.00005411554,0.00007837451,0.00007647566,0.000006685055,0.00005560249,0.0001594829,2.9849e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001083779,"about_ca_system_score_gemma":0.00003823323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002486482,"about_ca_topic_score_gemma":0.0005243552,"domain_scores_codex":[0.9990157,0.00004826699,0.0002637907,0.0003681389,0.0001036291,0.0002004531],"domain_scores_gemma":[0.9994175,0.00001636845,0.0001571615,0.0002578244,0.0001208905,0.00003029649],"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":[0.0001267165,0.0001633721,0.01339211,0.0002037935,0.00002368655,0.000001416781,0.00007287447,0.0009552916,0.9816346,0.001128604,0.0004603172,0.001837263],"study_design_scores_gemma":[0.00100295,0.0002080635,0.04102145,0.00003862427,0.00001211609,0.000004095768,0.00001768552,0.0004783752,0.9480601,0.002712891,0.006243234,0.0002003904],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6593377,0.0005114242,0.3107076,0.0003047703,0.00007714581,0.02879169,0.0001779688,0.0000188536,0.00007290827],"genre_scores_gemma":[0.9316383,0.000007954538,0.05886455,0.00007011033,0.0001683036,0.008923533,0.0002013906,0.00002547243,0.0001003765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2723006,"threshold_uncertainty_score":0.6288829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009099879848013536,"score_gpt":0.2672043744126486,"score_spread":0.2581044945646351,"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."}}