{"id":"W2165980173","doi":"10.1186/s13071-014-0622-3","title":"Functional analysis of Girardia tigrina transcriptome seeds pipeline for anthelmintic target discovery","year":2015,"lang":"en","type":"article","venue":"Parasites & Vectors","topic":"Planarian Biology and Electrostimulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Allergy and Infectious Diseases; Fonds de recherche du Québec – Nature et technologies; Office of Biotechnology, Iowa State University; Iowa State University; National Institutes of Health; National Institute of Nursing Research; Compute Canada","keywords":"Planarian; Biology; Planaria; Druggability; Computational biology; RNA interference; Flatworm; Transcriptome; Caenorhabditis elegans; Genetics; Anthelmintic; Evolutionary biology; Gene; Zoology; RNA","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":[0.0002066858,0.0001235714,0.000230417,0.0001377853,0.00004214692,0.000009557515,0.00008286368,0.0001376942,0.00001869101],"category_scores_gemma":[0.000110026,0.0001109727,0.0002014819,0.0002737084,0.00006304486,0.000009087492,0.00001126482,0.000045645,0.000004470462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001287575,"about_ca_system_score_gemma":0.00006312004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002126792,"about_ca_topic_score_gemma":0.00003616902,"domain_scores_codex":[0.999238,0.00004571658,0.0001991724,0.0002408277,0.00008463946,0.0001916987],"domain_scores_gemma":[0.9995573,0.00003918952,0.00007827063,0.000162984,0.00009817706,0.00006412851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007850575,0.00009107305,0.4313377,0.00001273996,0.0009460125,6.475315e-7,0.00005936619,0.008631166,0.5487062,0.0001362896,0.009175845,0.0001178569],"study_design_scores_gemma":[0.001394633,0.0007931128,0.6619995,0.000008163647,0.001490818,0.000007596411,0.00004604953,0.01173226,0.300419,0.0003708917,0.021296,0.0004419375],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9250035,0.0006137986,0.07348789,0.000191475,0.0001838707,0.0001289515,0.0001975309,0.00001028428,0.0001827307],"genre_scores_gemma":[0.9954273,0.00001420937,0.0002443042,0.0001575924,0.0001569211,0.00001527343,0.003208617,0.000009746057,0.0007660128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2482872,"threshold_uncertainty_score":0.4525335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207439557780792,"score_gpt":0.2680413458029486,"score_spread":0.2472973900248694,"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."}}