{"id":"W6969744267","doi":"10.5683/sp2/fveewe","title":"Peel-1 negative selection promotes screening-free CRISPR-Cas9 genome editing in Caenorhabditis elegans.","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Genome; Selection (genetic algorithm); Genome editing; Caenorhabditis elegans; Raw data; Code (set theory); Negative selection; Genome browser","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":[0.002036168,0.001121709,0.001277835,0.002975857,0.001724155,0.001542685,0.001594104,0.001259971,0.03759814],"category_scores_gemma":[0.002399023,0.001185411,0.00135626,0.001761866,0.0007773056,0.001409964,0.001509773,0.002987745,0.01974769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009816029,"about_ca_system_score_gemma":0.001533754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003756106,"about_ca_topic_score_gemma":0.01207003,"domain_scores_codex":[0.9979289,0.0001115629,0.0002189633,0.0006420502,0.0009235949,0.0001749092],"domain_scores_gemma":[0.9979295,0.0006874757,0.0003099336,0.0005548497,0.0003001245,0.0002181285],"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":[0.00149964,0.0002408906,0.003486145,0.002259301,0.0001750218,0.0004248051,0.000335501,0.001289063,0.8424248,0.002404819,0.1065899,0.03887018],"study_design_scores_gemma":[0.0002385245,0.0005139213,0.02276435,0.0003032334,0.0001650053,0.000794199,0.0001122154,0.005240475,0.778083,0.001222939,0.1903858,0.0001762591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1276184,0.001637088,0.1293559,0.001132857,0.001133643,0.001527789,0.6355829,0.0756947,0.02631676],"genre_scores_gemma":[0.1652761,0.001995242,0.2011525,0.001478116,0.00009772913,0.005179022,0.5274672,0.03479876,0.06255544],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03759814,"threshold_uncertainty_score":0.1257783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02256679336181826,"score_gpt":0.2678179235647295,"score_spread":0.2452511302029112,"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."}}