{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002016263,0.001625648,0.001663078,0.004351789,0.001090358,0.003352822,0.001306873,0.001505938,0.006844316],"category_scores_gemma":[0.003700183,0.001222449,0.001318745,0.0056422,0.0006944524,0.003095102,0.002875403,0.003297388,0.005685082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006064498,"about_ca_system_score_gemma":0.001215975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009319737,"about_ca_topic_score_gemma":0.001144121,"domain_scores_codex":[0.999199,0.0001581848,0.0000700493,0.0002918476,0.000206805,0.00007416087],"domain_scores_gemma":[0.9980518,0.0007543125,0.0002718253,0.0004910614,0.0002038955,0.0002271363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002343569,0.000402985,0.01437122,0.01590328,0.001548786,0.002323028,0.003517191,0.008342077,0.4004712,0.03966807,0.08859325,0.4225155],"study_design_scores_gemma":[0.0002489877,0.0002300959,0.02643314,0.001809662,0.0006462512,0.002546033,0.0009308244,0.008197246,0.08842906,0.06719113,0.8029963,0.0003412703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09084433,0.05684402,0.5617633,0.004547876,0.001188576,0.0004780505,0.2100942,0.05107993,0.02315969],"genre_scores_gemma":[0.09864356,0.02832139,0.5167555,0.0007859701,0.0003726048,0.0005513332,0.3392111,0.01063652,0.004722128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006844316,"threshold_uncertainty_score":0.02289653,"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."}}