{"id":"W6920913615","doi":"10.6084/m9.figshare.12421364.v1","title":"Additional file 1 of MAUDE: inferring expression changes in sorting-based CRISPR screens","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"CRISPR; Expression (computer science); Gene expression; Key (lock); DNA","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003433826,0.001289412,0.001632008,0.003914995,0.0007771494,0.002208861,0.002558105,0.001613296,0.7565001],"category_scores_gemma":[0.03653492,0.0008658222,0.001347507,0.004254807,0.0003850368,0.002009038,0.001308348,0.00120353,0.1777529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001280913,"about_ca_system_score_gemma":0.002341708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004039896,"about_ca_topic_score_gemma":0.007883689,"domain_scores_codex":[0.9987192,0.0002658477,0.0001985349,0.0003620662,0.000356191,0.00009803524],"domain_scores_gemma":[0.9631423,0.03126678,0.001275514,0.001636669,0.002169258,0.0005096304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002076802,0.00003255287,0.001052888,0.00531301,0.00007866207,0.00007343013,0.00005114374,0.0004443773,0.0003400612,0.00114466,0.9787078,0.01255385],"study_design_scores_gemma":[0.001214303,0.0001335373,0.006757515,0.002927542,0.0003197841,0.0005106826,0.0001246573,0.001284353,0.001784905,0.01033849,0.9744776,0.000126635],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0001276095,0.0001752176,0.00122234,0.0002005673,0.00005934997,0.00006574037,0.9950035,0.001675969,0.001469707],"genre_scores_gemma":[0.007867573,0.000936307,0.0120337,0.001207797,0.0002071301,0.001630289,0.9601519,0.004249282,0.01171604],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7565001,"threshold_uncertainty_score":0.347323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02580801185670193,"score_gpt":0.3153792072701039,"score_spread":0.2895711954134019,"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."}}