{"id":"W2758550728","doi":"10.1111/eva.12559","title":"Assessing the potential of genotyping‐by‐sequencing‐derived single nucleotide polymorphisms to identify the geographic origins of intercepted gypsy moth (<i>Lymantria dispar</i>) specimens: A proof‐of‐concept study","year":2017,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Université Laval; Natural Resources Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lymantria dispar; Gypsy moth; Subspecies; Biology; Biological dispersal; Population; Ecology; Range (aeronautics); Introgression; Genotyping; Population genetics; Introduced species; Zoology; Evolutionary biology; Lepidoptera genitalia; Genetics; Genotype; Demography","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":[0.004520294,0.0008414813,0.000465777,0.0005139256,0.0002718618,0.0007699127,0.0006973865,0.0008434975,0.001116667],"category_scores_gemma":[0.002459679,0.000317415,0.0007382513,0.0001688935,0.0005055804,0.0004881074,0.0005312822,0.0009610339,0.0004346632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002135338,"about_ca_system_score_gemma":0.0004384266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004357741,"about_ca_topic_score_gemma":0.0009027588,"domain_scores_codex":[0.9980819,0.0006153189,0.0001153705,0.000450432,0.000605584,0.0001313278],"domain_scores_gemma":[0.9969361,0.001053364,0.0006804009,0.0003016932,0.0008090995,0.0002194411],"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":[0.0002667212,0.0004984053,0.008316742,0.0001599368,0.00008617681,0.00006541982,0.00005682337,0.0005223486,0.9823445,0.0001329429,0.000159206,0.007390662],"study_design_scores_gemma":[0.0002401289,0.01057019,0.05765965,0.00006597533,0.0004394882,0.00126125,0.0002718423,0.01409219,0.9092987,0.0003870049,0.005641684,0.00007198043],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9257976,0.0007315699,0.06859247,0.0002154593,0.0001336424,0.001294482,0.001588023,0.0002569007,0.001389861],"genre_scores_gemma":[0.8911504,0.0004840657,0.1026589,0.0004352945,0.00004735179,0.001068194,0.002136376,0.00006783575,0.001951537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004520294,"threshold_uncertainty_score":0.02390587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01820047732686663,"score_gpt":0.2813392855840053,"score_spread":0.2631388082571386,"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."}}