{"id":"W3153688655","doi":"10.20944/preprints202104.0531.v1","title":"Omics in Major Cereals: Applications, Challenges, and Prospects","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Phenomics; Omics; Proteomics; Metabolomics; Biology; Biotechnology; Genomics; Abiotic stress; Computational biology; Bioinformatics; Genome; Genetics; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002785804,0.0002249615,0.0002690318,0.00005875164,0.0000540193,0.00002476402,0.0002847653,0.0003841254,0.00004221641],"category_scores_gemma":[0.00005592023,0.0002510065,0.00007588328,0.00003362859,0.00005634164,0.000002066416,0.001620118,0.0002795852,0.00003028042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001757641,"about_ca_system_score_gemma":0.0001056867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008508237,"about_ca_topic_score_gemma":0.00009490348,"domain_scores_codex":[0.9984654,0.00006220696,0.0002413879,0.0008890737,0.0001131155,0.0002288186],"domain_scores_gemma":[0.9990404,0.000009850591,0.0001197363,0.0006627626,0.00007015208,0.0000970686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001447481,0.0006754509,0.7831409,0.001728466,0.0003703827,0.00005257939,0.001638176,0.0002130758,0.1974938,0.001361951,0.0001729789,0.01300751],"study_design_scores_gemma":[0.001078821,0.00005682161,0.8489943,0.0002838471,0.00007921619,0.00004559069,0.001354659,0.00003999143,0.07270736,0.001652314,0.0727856,0.0009214588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740247,0.0166444,0.00005203995,0.0002972549,0.0001057914,0.0004922686,0.00004599436,0.00002045261,0.008317141],"genre_scores_gemma":[0.9665517,0.03165136,0.0003674727,0.00009684852,0.0001627545,0.0001234802,0.0002930094,0.00001651635,0.0007368681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1247864,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07819928476011342,"score_gpt":0.3004007254194931,"score_spread":0.2222014406593797,"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."}}