{"id":"W3041946983","doi":"10.1016/j.molbiopara.2020.111295","title":"Bar-seq strategies for the LeishGEdit toolbox","year":2020,"lang":"en","type":"article","venue":"Molecular and Biochemical Parasitology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Medical Research Council Canada; Royal Society","keywords":"CRISPR; Biology; Computational biology; Genome; Pipeline (software); Barcode; Function (biology); Genome editing; Genetics; Gene; Computer science; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005461788,0.000154242,0.0001450845,0.000009320886,0.00006694315,0.00003294127,0.0001801741,0.0001701648,0.000008362114],"category_scores_gemma":[0.00009547349,0.000118364,0.0000950056,0.00004794653,0.0001366681,0.000002018385,0.00006973978,0.00009557226,0.000003622037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002387213,"about_ca_system_score_gemma":0.00003173267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003933773,"about_ca_topic_score_gemma":0.000001501987,"domain_scores_codex":[0.9991933,0.00001860405,0.0001415014,0.0003402515,0.00006357745,0.0002428313],"domain_scores_gemma":[0.9996139,0.00003125517,0.0000324152,0.0001782373,0.00003612272,0.0001080674],"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":[0.00008069857,0.00001014062,0.000307138,0.00002664273,0.00005872701,0.000003869979,0.00003492032,0.0001411665,0.9917712,0.0007576657,0.006153997,0.0006538127],"study_design_scores_gemma":[0.0005512604,0.0003104102,0.0006805857,0.000003000138,0.00005597418,0.00003720797,0.0001730496,0.0007844573,0.9645688,0.0003355808,0.03226958,0.0002300738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7108345,0.005562146,0.2787407,0.004091975,0.000137519,0.0002642246,0.00001676741,0.00002219643,0.0003299158],"genre_scores_gemma":[0.9964474,0.00007052101,0.001189633,0.001824366,0.0003317825,0.00004473224,0.00005307361,0.0000178559,0.0000205853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2856129,"threshold_uncertainty_score":0.4826742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057190531115937,"score_gpt":0.3092427184433535,"score_spread":0.2986708131321941,"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."}}