{"id":"W4317492674","doi":"10.1101/2023.01.18.524633","title":"A multi-kingdom genetic barcoding system for precise target clone isolation","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"CRISPR; Biology; Barcode; clone (Java method); Computational biology; DNA barcoding; Population; Genetics; Gene; Phenotype; Evolutionary biology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001163973,0.0005969364,0.0007338616,0.0008266069,0.0005579168,0.001103159,0.0008957522,0.001385624,0.002569892],"category_scores_gemma":[0.001466536,0.0007400926,0.0005954141,0.000452301,0.0007146312,0.0008893247,0.002066719,0.002218747,0.003179085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005974763,"about_ca_system_score_gemma":0.0005345265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003740444,"about_ca_topic_score_gemma":0.001020557,"domain_scores_codex":[0.9986287,0.0001768153,0.0001172848,0.0003774276,0.0005827241,0.0001171143],"domain_scores_gemma":[0.9986333,0.0002905015,0.0003256075,0.0004619967,0.000157084,0.0001314094],"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":[0.0000308089,0.00001136672,0.0001329042,0.00005797159,0.000008305936,0.00004814992,0.00003887266,0.0001376766,0.9901126,0.001217783,0.0005270193,0.007676642],"study_design_scores_gemma":[0.00001057759,0.0000672923,0.0007142998,0.00001490286,0.00001576321,0.0004854944,0.00001304603,0.002640174,0.9745039,0.0003046301,0.0212047,0.00002513376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09956595,0.00166472,0.8804264,0.000634867,0.000465282,0.0003733187,0.002010099,0.00892126,0.005938059],"genre_scores_gemma":[0.4010794,0.001910698,0.5721807,0.000722615,0.00009871951,0.000663932,0.005618355,0.002488898,0.01523667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002569892,"threshold_uncertainty_score":0.008597136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01998040965481572,"score_gpt":0.268056199175754,"score_spread":0.2480757895209383,"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."}}