{"id":"W4288423205","doi":"10.1126/science.abo3471","title":"Molecular recorders to track cellular events","year":2022,"lang":"en","type":"article","venue":"Science","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Track (disk drive); Computational biology; Computer science; Biology","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":[0.002088168,0.0006254981,0.0006789804,0.00175793,0.0006515215,0.001940386,0.001673981,0.001955887,0.009670728],"category_scores_gemma":[0.005324536,0.0006329462,0.0004886947,0.001619554,0.0009649065,0.00241724,0.001254643,0.002616595,0.005541063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007094824,"about_ca_system_score_gemma":0.0007459995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005888484,"about_ca_topic_score_gemma":0.0009380316,"domain_scores_codex":[0.9989033,0.000189066,0.00007994036,0.0002575002,0.0004905245,0.00007977732],"domain_scores_gemma":[0.994602,0.001648101,0.0008094106,0.001401381,0.001186236,0.0003528395],"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":[0.0007345593,0.0001185895,0.002118621,0.001092455,0.0001508793,0.0004037181,0.0004300234,0.001634605,0.7595913,0.03158892,0.01995436,0.1821819],"study_design_scores_gemma":[0.000139284,0.0008029333,0.004026422,0.0004081349,0.0002397163,0.001386008,0.0003084062,0.01105349,0.6958429,0.0153703,0.2702424,0.0001801395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08347016,0.02483456,0.8197651,0.004818171,0.007312498,0.0006595646,0.00953243,0.01439524,0.03521222],"genre_scores_gemma":[0.3430406,0.0208166,0.5132342,0.003418022,0.001599577,0.001072194,0.006594945,0.001215434,0.1090083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009670728,"threshold_uncertainty_score":0.03235185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005378074050114524,"score_gpt":0.2811484154650964,"score_spread":0.2757703414149819,"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."}}