{"id":"W2905627470","doi":"10.1002/ece3.4803","title":"Repurposing population genetics data to discern genomic architecture: A case study of linkage cohort detection in mountain pine beetle (<i>Dendroctonus ponderosae</i>)","year":2018,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Island University; University of Alberta","funders":"U.S. Forest Service; Ministry of Environment; Ministry of Environment - Saskatchewan; Alberta Agriculture and Forestry; Canadian Forest Service; Ontario Ministry of Natural Resources and Forestry; Natural Resources Canada; University of Alberta; Université Laval; Weyerhaeuser Company","keywords":"Dendroctonus; Mountain pine beetle; Repurposing; Biology; Population genetics; Linkage (software); Population; Evolutionary biology; Ecology; Genomics; Geography; Genetics; Genome; Bark beetle; Medicine; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004978543,0.0002779146,0.0002818604,0.001814524,0.0008604549,0.0008127324,0.0006144491,0.0005028308,0.0005160615],"category_scores_gemma":[0.01068015,0.0001715766,0.0004926785,0.001863862,0.0007222095,0.0003728179,0.0006275917,0.0007516702,0.00008983084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009010878,"about_ca_system_score_gemma":0.001142362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09825182,"about_ca_topic_score_gemma":0.2284354,"domain_scores_codex":[0.9989492,0.0005511296,0.00004314937,0.0001813579,0.0001778167,0.00009741552],"domain_scores_gemma":[0.993189,0.004645417,0.0005259021,0.000671213,0.0007479539,0.000220636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001446449,0.0001522777,0.8455874,0.0001207695,0.0003323435,0.003046436,0.004203159,0.009643584,0.02106152,0.002247196,0.0006066627,0.112854],"study_design_scores_gemma":[0.00003448659,0.0001584918,0.8857942,0.00005872921,0.0002800371,0.002693893,0.003616894,0.09034392,0.007545707,0.00513461,0.004254928,0.00008405249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647897,0.0002617495,0.03339621,0.0002096724,0.000005921408,0.00008919815,0.0002678831,0.00008422912,0.0008954824],"genre_scores_gemma":[0.9330063,0.0001261082,0.06616065,0.00005727914,0.000006085606,0.00002204499,0.0002713289,0.00003380239,0.0003163458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09825182,"threshold_uncertainty_score":0.1953599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425887616578509,"score_gpt":0.2521123362750909,"score_spread":0.2378534601093058,"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."}}