{"id":"W2898001247","doi":"10.1101/439612","title":"Comparative whole-genome analysis reveals genetic adaptation of the invasive pinewood nematode","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Nematode management and characterization studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; Canadian Institutes of Health Research; Chinese Academy of Agricultural Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Agricultural Science and Technology Innovation Program; Michael Smith Health Research BC","keywords":"Biology; Genetics; Nucleotide diversity; Genetic variation; Population; Genetic diversity; Evolutionary biology; Adaptation (eye); Gene; Genome; Single-nucleotide polymorphism; Phylogenetic tree; Allele; Genotype; Haplotype","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001263754,0.0001675848,0.0001849494,0.0007207591,0.0001786147,0.0002231166,0.0001109875,0.0001561641,0.0006954359],"category_scores_gemma":[0.000178118,0.00008598753,0.0003072233,0.0006659993,0.0000940994,0.00008778022,0.0001328367,0.000194128,0.0001053344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009595681,"about_ca_system_score_gemma":0.00009825481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001510437,"about_ca_topic_score_gemma":0.003040752,"domain_scores_codex":[0.9999158,0.00001057032,0.000006450693,0.00003511038,0.00001886466,0.00001323984],"domain_scores_gemma":[0.9998627,0.00003558326,0.00004974689,0.000009089653,0.00001753325,0.0000254138],"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":[0.0003860515,0.00005185433,0.06264825,0.0001592938,0.0002201985,0.0003711819,0.0001636012,0.0003561927,0.9266438,0.00007751395,0.0001615064,0.00876056],"study_design_scores_gemma":[0.000008034561,0.0001297101,0.9851783,0.000009083466,0.0001135498,0.0004422118,0.0001207229,0.0008715751,0.0117905,0.00003597772,0.00129342,0.000006933838],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972016,0.0004018181,0.0008777498,0.000009927667,0.000004486295,0.000006993337,0.001228479,0.00001676967,0.0002521066],"genre_scores_gemma":[0.9934754,0.000274869,0.002048185,0.00002721987,0.000006039675,0.00001124432,0.003737976,0.00001057338,0.0004085269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001510437,"threshold_uncertainty_score":0.003003299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340140564369376,"score_gpt":0.223514162495879,"score_spread":0.1901127568521852,"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."}}