{"id":"W4386089587","doi":"10.20944/preprints202308.1641.v1","title":"Exploring Pan-Genomes of Crops: Genomic Resources and Tools for Unraveling Gene Evolution, Function, and Adaptation","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Agricultural University; Nuclear Safety and Security Commission; New Zealand Institute for Plant and Food Research Limited; Strong; National Science Foundation; Ontario Institute for Cancer Research; National Aeronautics and Space Administration","keywords":"Domestication; Biology; Compendium; Genome; Adaptation (eye); Genomics; Molecular breeding; Selection (genetic algorithm); Function (biology); Gene; Biotechnology; Evolutionary biology; Genetics; Geography; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004908455,0.000258232,0.0003219213,0.00009901733,0.0001547358,0.00003365501,0.0001705657,0.0001881435,0.00000275234],"category_scores_gemma":[0.0002558226,0.0002846349,0.0001056325,0.00004874155,0.0001173968,0.000004104133,0.001163628,0.0001201974,0.000004433746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002753798,"about_ca_system_score_gemma":0.00008032592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001412183,"about_ca_topic_score_gemma":0.00005367884,"domain_scores_codex":[0.9983465,0.00004872848,0.000475811,0.0007967672,0.00009892154,0.0002332509],"domain_scores_gemma":[0.9988854,0.00006345285,0.0002919468,0.0004736262,0.0002225489,0.0000630537],"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":[0.0001576659,0.00002173233,0.1938121,0.0002980817,0.0004071987,2.891775e-7,0.001107542,0.003051157,0.7981202,0.0001059234,0.000005813096,0.002912301],"study_design_scores_gemma":[0.0004516531,0.00009955422,0.9450532,0.00005885484,0.0001475458,0.000002704364,0.001349336,0.0002667131,0.04643042,0.003303391,0.002497829,0.0003387612],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898607,0.005828402,0.003087287,0.00007161525,0.0003835478,0.0005995854,0.0001107519,0.00001422132,0.00004394228],"genre_scores_gemma":[0.9895934,0.007818797,0.001609637,0.00001778349,0.0003425711,0.0003343497,0.0001036608,0.00005044045,0.0001293852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7516897,"threshold_uncertainty_score":0.9999606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2500510409916588,"score_gpt":0.3062309832945746,"score_spread":0.05617994230291579,"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."}}