{"id":"W2741011090","doi":"10.1111/eva.12524","title":"Applications of random forest feature selection for fine‐scale genetic population assignment","year":2017,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Fisheries and Oceans Canada; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Random forest; Selection (genetic algorithm); Biology; Oncorhynchus; Population; Salmo; SNP; Statistics; Artificial intelligence; Single-nucleotide polymorphism; Computer science; Mathematics; Genetics; Fishery; Fish <Actinopterygii>; Genotype","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":[0.005748709,0.0007335225,0.0007800537,0.001715961,0.0004174134,0.000561563,0.0008267431,0.0005805622,0.001122633],"category_scores_gemma":[0.01101514,0.0002148771,0.0007202977,0.0008853847,0.0003600856,0.0005458666,0.0005908152,0.0007232374,0.0004393807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004275567,"about_ca_system_score_gemma":0.0006110001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004621784,"about_ca_topic_score_gemma":0.004859146,"domain_scores_codex":[0.9977511,0.00142078,0.0001102993,0.0003661558,0.0002343357,0.0001174687],"domain_scores_gemma":[0.9919673,0.005617307,0.000452393,0.0006520149,0.001191679,0.000119385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006827653,0.0005117418,0.03309757,0.0001289841,0.0003571111,0.0002253409,0.0001126533,0.501208,0.02029785,0.002424999,0.004086135,0.4368668],"study_design_scores_gemma":[0.00002776518,0.00006907889,0.00302749,0.00000563401,0.0000165098,0.00003146051,0.00001063153,0.9926192,0.002252488,0.001626591,0.0003024649,0.00001067039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1547444,0.0002012296,0.8410081,0.000135068,0.00003086504,0.0001510253,0.00047899,0.002357772,0.0008924971],"genre_scores_gemma":[0.6756076,0.00003555932,0.3226794,0.00008299168,0.00002510127,0.0001813564,0.0008325923,0.000115462,0.0004400208],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005748709,"threshold_uncertainty_score":0.03040242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006527567886376638,"score_gpt":0.2509173471517909,"score_spread":0.2443897792654143,"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."}}