{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220641,0.0001401611,0.0001557521,0.0000491141,0.000327211,0.00006301136,0.0002898985,0.00002735071,0.000008930735],"category_scores_gemma":[0.00004250733,0.0001034937,0.0001093658,0.0001546131,0.00001972703,0.000002404335,0.0001094975,0.00008393123,0.00000112058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001728803,"about_ca_system_score_gemma":0.00002760859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003220478,"about_ca_topic_score_gemma":0.0001072977,"domain_scores_codex":[0.9991572,0.00004369236,0.0001148318,0.0002718731,0.0001399128,0.0002724982],"domain_scores_gemma":[0.9994219,0.0000295203,0.00004277941,0.0003871062,0.00005554096,0.00006313899],"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":[0.0004777861,0.00002621401,0.001303019,0.00004593287,0.0005234613,0.000006940648,0.0002582216,0.06841763,0.916326,0.00005835572,0.008461154,0.00409524],"study_design_scores_gemma":[0.001852116,0.001747642,0.001174807,0.00001876879,0.0003928903,0.00006702991,0.001818068,0.002030757,0.5233728,0.0000614293,0.4668137,0.0006499875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3279934,0.001670275,0.6669837,0.002308862,0.0001783931,0.0006697123,0.00004775296,0.00003075811,0.00011721],"genre_scores_gemma":[0.996619,0.00001291153,0.001325871,0.0006343299,0.0003353721,0.0002846642,0.00002119742,0.00003239982,0.0007341946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6686257,"threshold_uncertainty_score":0.4220349,"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."}}