{"id":"W2944166269","doi":"10.1101/630236","title":"The Vertebrate Codex Gene Breaking Protein Trap Library For Genomic Discovery and Disease Modeling Applications","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Mayo Foundation for Medical Education and Research; National Human Genome Research Institute; Mayo Clinic","keywords":"Biology; Genetics; Forward genetics; Gene; Zebrafish; Insertional mutagenesis; Genetic screen; Gene knockdown; Phenotype; Locus (genetics); Positional cloning; Transposable element; Mutagenesis; Mutant","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000612886,0.0008502565,0.000633044,0.001540717,0.0006093043,0.000881888,0.0007976397,0.0009118984,0.0108671],"category_scores_gemma":[0.0002842129,0.0006386281,0.0008627759,0.0007960409,0.0003244662,0.0002985732,0.0008147004,0.001117541,0.007309128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004733541,"about_ca_system_score_gemma":0.0007328364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001184591,"about_ca_topic_score_gemma":0.003000262,"domain_scores_codex":[0.9994923,0.00004732726,0.00003665547,0.0001136015,0.0002505981,0.00005944195],"domain_scores_gemma":[0.9997578,0.0000361422,0.00004764055,0.00006367094,0.00004022065,0.0000545297],"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":[0.00006950579,0.00003699647,0.0002483445,0.00009785505,0.00001850666,0.0001451467,0.00002689551,0.0002866189,0.9858447,0.0008629713,0.001567566,0.01079495],"study_design_scores_gemma":[0.0001029496,0.0004521009,0.004095113,0.00009346559,0.0001383387,0.001387988,0.00003427083,0.006475059,0.8755506,0.0005908796,0.1110338,0.00004558726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2243982,0.004277079,0.6584846,0.001100749,0.0004256532,0.002910222,0.05627801,0.02258648,0.02953897],"genre_scores_gemma":[0.3865514,0.004408763,0.4636383,0.0005323011,0.00008663777,0.002907903,0.08138587,0.003513697,0.05697515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0108671,"threshold_uncertainty_score":0.03635401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007501791575728955,"score_gpt":0.2254073325198416,"score_spread":0.2179055409441127,"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."}}