{"id":"W4408244362","doi":"10.1111/1755-0998.14096","title":"<i>Popfinder</i>: A Highly Effective Artificial Neural Network Package for Genetic Population Assignment","year":2025,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Environment and Climate Change Canada","keywords":"Population; Computer science; Overfitting; Artificial neural network; Python (programming language); Machine learning; Artificial intelligence; Data mining; Biology","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":[0.0024377,0.002325851,0.001161319,0.00156079,0.0008277786,0.001649536,0.004424142,0.0012228,0.06478997],"category_scores_gemma":[0.009939426,0.001515091,0.002093226,0.001477913,0.000954397,0.002837113,0.003246959,0.004301814,0.02257067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013581,"about_ca_system_score_gemma":0.002892317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005438997,"about_ca_topic_score_gemma":0.006525021,"domain_scores_codex":[0.9990564,0.0001611195,0.0001037413,0.0002346866,0.0003409677,0.000103052],"domain_scores_gemma":[0.9975555,0.001292076,0.0003038021,0.000291199,0.0004418,0.0001156068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004094603,0.0001740223,0.004515088,0.002175804,0.0004794838,0.0005265563,0.0005058533,0.04317269,0.008451908,0.01462333,0.698762,0.2262038],"study_design_scores_gemma":[0.0004008026,0.0001253806,0.004532501,0.0003427764,0.0001129118,0.0004924438,0.00007910632,0.6046362,0.02983611,0.0592774,0.2998795,0.000284704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004008718,0.0002314877,0.4780252,0.0005235267,0.0003634901,0.0003584918,0.03553732,0.4764516,0.004500195],"genre_scores_gemma":[0.03790278,0.0004768338,0.7680638,0.001567801,0.000162366,0.003216056,0.05241746,0.1235391,0.01265365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06478997,"threshold_uncertainty_score":0.2167441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005240479217983405,"score_gpt":0.227964874524488,"score_spread":0.2227243953065046,"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."}}