{"id":"W2920525488","doi":"10.1038/s42003-019-0349-y","title":"Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts","year":2019,"lang":"en","type":"erratum","venue":"Communications Biology","topic":"Animal Genetics and Reproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Hospital for Sick Children; Toronto Centre for Phenogenomics","funders":"National Eye Institute; National Institutes of Health; Agence Nationale de la Recherche","keywords":"Identification (biology); Gene knockout; Computer science; Throughput; Computational biology; Library science; Genetics; Biology; Gene; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004165047,0.0002335373,0.0004130032,0.0001115039,0.0001774687,0.00000982355,0.000891363,0.0007598187,0.000007773691],"category_scores_gemma":[0.0001787633,0.0002377607,0.0001465826,0.0001286289,0.0003411843,0.000004099457,0.0004954701,0.000188559,0.000004691211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000279453,"about_ca_system_score_gemma":0.0002776817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004368423,"about_ca_topic_score_gemma":0.00003836588,"domain_scores_codex":[0.9981501,0.0001598935,0.0008736422,0.0005345655,0.00008602859,0.000195738],"domain_scores_gemma":[0.9961403,0.00003502009,0.001035958,0.00211237,0.0006405606,0.00003581736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005995214,0.0001196533,0.000244326,0.00009993101,0.0002010443,7.820757e-9,0.00005307795,0.00002706279,0.7483925,0.0001329513,0.2430297,0.007639789],"study_design_scores_gemma":[0.0001777084,0.0002289321,0.0001894262,0.00003147852,0.00005327617,0.000001207105,0.00005832011,0.0001771546,0.3327073,0.00002283106,0.6661725,0.0001798827],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1225998,0.2170168,0.5680395,0.005085652,0.0677637,0.008671096,0.005576774,0.0001996318,0.005047039],"genre_scores_gemma":[0.3202851,0.01363736,0.02961918,0.00005722763,0.0008401842,0.0004429589,0.04243447,0.0001303746,0.5925531],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5875061,"threshold_uncertainty_score":0.9695599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957473164699039,"score_gpt":0.3260252487006716,"score_spread":0.2864505170536812,"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."}}