{"id":"W4403486381","doi":"10.1111/1755-0998.14024","title":"What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of <scp>ResistanceGA</scp>","year":2024,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation; Agence Nationale de la Recherche","keywords":"Overfitting; Population; Biology; Reliability (semiconductor); Inference; Biological dispersal; Computer science; Statistics; Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000320566,0.0001930194,0.0002231832,0.00006716832,0.0002281769,0.0001606481,0.0002135295,0.0001049508,0.0000797414],"category_scores_gemma":[0.0003294064,0.0001410907,0.000058552,0.0002617931,0.0004665617,0.0001504839,0.00006015726,0.0001639151,0.00001736569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008093143,"about_ca_system_score_gemma":0.00001840662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003582043,"about_ca_topic_score_gemma":0.001105832,"domain_scores_codex":[0.9982577,0.0005129306,0.0002653284,0.0004669175,0.0002545647,0.0002425403],"domain_scores_gemma":[0.9971836,0.002309584,0.0001182653,0.000318347,0.00001422715,0.00005602283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001971051,0.0003933989,0.6793665,0.00002613866,0.00009557445,0.0001330635,0.002821665,0.3150754,0.000498803,0.0001217999,0.0006120474,0.0006585062],"study_design_scores_gemma":[0.0006426628,0.0005588486,0.9206268,0.00006112827,0.0000834161,0.000001199258,0.001440992,0.07172222,0.0002908816,0.0002098541,0.004266702,0.00009531074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968341,0.0002899886,0.0004895905,0.00116471,0.0001700935,0.0008139174,0.00001319431,0.00003969248,0.0001846973],"genre_scores_gemma":[0.9980996,0.00003057224,0.0001253775,0.001303369,0.00001367037,0.0001428322,0.000006821151,0.00001797771,0.0002597839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2433532,"threshold_uncertainty_score":0.5753512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827762137247443,"score_gpt":0.2410017108935588,"score_spread":0.2227240895210844,"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."}}