{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005787219,0.0007629501,0.0007477205,0.0006791714,0.0005334253,0.000584925,0.0005341442,0.0005046668,0.0006682284],"category_scores_gemma":[0.0003228594,0.0003443133,0.0005012724,0.0004265188,0.000325038,0.0002424255,0.0003506901,0.0005963907,0.000342292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006876322,"about_ca_system_score_gemma":0.0006617876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001992771,"about_ca_topic_score_gemma":0.006905938,"domain_scores_codex":[0.9996487,0.00002723718,0.00002608177,0.00005106299,0.0002134881,0.00003353685],"domain_scores_gemma":[0.999806,0.00006516541,0.00003396374,0.00002173114,0.00003960847,0.00003360585],"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":[0.00006701334,0.00004221865,0.0001094938,0.00004207191,0.000009444877,0.00002760849,0.000004669168,0.0002301927,0.9965078,0.00003296183,0.00007180176,0.002854718],"study_design_scores_gemma":[0.00003792319,0.0003780604,0.001257066,0.000005659926,0.00004209963,0.00009384496,0.00000978315,0.001273168,0.9953362,0.00003621097,0.00151813,0.00001175298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9607773,0.005348935,0.02699502,0.0003111686,0.00005962529,0.000646945,0.003102166,0.0007334753,0.002025369],"genre_scores_gemma":[0.9205011,0.006958219,0.06121499,0.0001858215,0.00002454185,0.0006441576,0.004769734,0.00008337719,0.005618025],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001992771,"threshold_uncertainty_score":0.004989147,"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."}}