{"id":"W4405299142","doi":"","title":"Toward a high-resolution functional annotation of the cattle genome using novel breeds/crosses","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Annotation; Genome; High resolution; Biology; Computer science; Computational biology; Resolution (logic); Genetics; Artificial intelligence; Geography; Gene; Remote sensing","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":[0.002142611,0.001206852,0.001804666,0.001711859,0.001005027,0.002808434,0.001503755,0.001639674,0.006981843],"category_scores_gemma":[0.002197884,0.0007233098,0.002063889,0.001997164,0.000420809,0.001284368,0.001596148,0.002406236,0.003746748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005449737,"about_ca_system_score_gemma":0.00072898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001963689,"about_ca_topic_score_gemma":0.003352677,"domain_scores_codex":[0.9993024,0.0001244741,0.00004303006,0.000286777,0.0001483618,0.00009507051],"domain_scores_gemma":[0.9984934,0.0006363091,0.0002159565,0.0002224892,0.0002458277,0.0001860909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004441827,0.00008478223,0.002357778,0.0008016819,0.0001769145,0.000520248,0.0003192872,0.001981309,0.9596887,0.001286696,0.00177915,0.03055922],"study_design_scores_gemma":[0.0004859212,0.0014151,0.1885686,0.001297449,0.002443997,0.005510866,0.002013425,0.05616581,0.4635975,0.0173499,0.2606747,0.0004766776],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4147002,0.007784511,0.510338,0.002148817,0.0008670223,0.0002876053,0.04852596,0.006138012,0.009209911],"genre_scores_gemma":[0.3707785,0.005650341,0.471079,0.001345128,0.0003369316,0.0003216858,0.1393344,0.003251425,0.007902574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006981843,"threshold_uncertainty_score":0.02335662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031191619151252,"score_gpt":0.2141183445387429,"score_spread":0.1938064283472304,"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."}}