{"id":"W4308370161","doi":"10.1039/d2an01318e","title":"How to detect CRISPR with CRISPR – employing SHERLOCK for doping control purposes","year":2022,"lang":"en","type":"article","venue":"The Analyst","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Sporthochschule Köln; Manfred Donike Institut für Dopinganalytik; World Anti-Doping Agency","keywords":"CRISPR; Genome editing; Computational biology; Guide RNA; Cas9; Trans-activating crRNA; Subgenomic mRNA; Nucleic acid; DNA; Biology; Computer science; Gene; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001459838,0.0006970871,0.0007986348,0.0004777127,0.0003967977,0.001807699,0.000713421,0.001565131,0.003435901],"category_scores_gemma":[0.002846471,0.0006500135,0.0006575261,0.0002286961,0.0006600127,0.001047618,0.0006513529,0.001384022,0.002425991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004234677,"about_ca_system_score_gemma":0.0007991989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266186,"about_ca_topic_score_gemma":0.002084148,"domain_scores_codex":[0.9990256,0.0001576526,0.00005815655,0.0003022783,0.0003384578,0.000117954],"domain_scores_gemma":[0.998794,0.0004814647,0.0002031646,0.0001942495,0.0002327664,0.00009444205],"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":[0.0001160698,0.0000403891,0.001397611,0.0002396022,0.00003676087,0.000165588,0.00007017505,0.001658678,0.9691486,0.001830971,0.001011015,0.02428455],"study_design_scores_gemma":[0.00001297391,0.000152166,0.0008553605,0.00002621568,0.00003258868,0.0003324182,0.00005201005,0.009954691,0.9780257,0.001018535,0.00949166,0.00004569388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2607801,0.002993645,0.7088137,0.002868874,0.0007804579,0.0004778539,0.001827084,0.01151323,0.009945094],"genre_scores_gemma":[0.589028,0.002236034,0.3939767,0.0009456492,0.00006789206,0.0003574388,0.001335666,0.0009972147,0.01105542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003435901,"threshold_uncertainty_score":0.01149428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00871881426582139,"score_gpt":0.2748541392129236,"score_spread":0.2661353249471022,"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."}}