{"id":"W3136386787","doi":"10.1101/2021.03.16.435597","title":"Fitness costs and benefits of gene expression plasticity in rice under drought","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Gordon and Betty Moore Foundation; Life Sciences Research Foundation; New York University Abu Dhabi; Fordham University; Zegar Family Foundation; National Science Foundation","keywords":"Biology; Gene; Selection (genetic algorithm); Housekeeping gene; Gene expression; Plasticity; Phenotypic plasticity; Natural selection; Genetics; Genome; Regulation of gene expression; Evolutionary biology; Computational biology; 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":[0.0003595734,0.0003049698,0.0002803098,0.0002287246,0.0001494024,0.0004010104,0.0002358946,0.0002199603,0.0009925721],"category_scores_gemma":[0.0003286435,0.0001373164,0.000166034,0.0001774667,0.000296197,0.0002096275,0.0005564407,0.000540684,0.0001101069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003595692,"about_ca_system_score_gemma":0.0001557511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003432712,"about_ca_topic_score_gemma":0.0007002529,"domain_scores_codex":[0.9998301,0.00003603932,0.00001529261,0.0000472343,0.00003607824,0.00003522774],"domain_scores_gemma":[0.9997703,0.00007289676,0.00004877223,0.00003884821,0.0000189785,0.00005025094],"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":[0.00009744708,0.00001062765,0.002959817,0.00001647785,0.00001583777,0.00005606535,0.00001712416,0.0005679961,0.9940509,0.0002247328,0.00002883625,0.001954077],"study_design_scores_gemma":[0.00004748846,0.0007088025,0.4567721,0.00001519276,0.0001355139,0.0009402927,0.0003183915,0.02705444,0.5077119,0.003023043,0.003191168,0.00008168535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978017,0.00008046553,0.001497748,0.00004871688,0.000004077991,0.00000238206,0.0001536748,0.00004351247,0.0003676573],"genre_scores_gemma":[0.9990175,0.00003669456,0.0004761239,0.00002956065,0.000002877031,0.00000588322,0.00009409687,0.00002268435,0.000314584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009925721,"threshold_uncertainty_score":0.003320456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00962237598016844,"score_gpt":0.2380461561865727,"score_spread":0.2284237802064042,"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."}}