{"id":"W4396930748","doi":"10.5852/ejt.2024.934.2529","title":"A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons","year":2024,"lang":"en","type":"article","venue":"European Journal of Taxonomy","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Morphometrics; Software; Procrustes analysis; User Friendly; Group (periodic table); Geography; Computer science; Artificial intelligence; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002408296,0.0003823707,0.0007610072,0.001252491,0.0002562315,0.0005819542,0.0004630731,0.00007435074,0.0002387732],"category_scores_gemma":[0.00259612,0.0003150487,0.0002385799,0.001968853,0.0001034473,0.0004981992,0.0003546022,0.0007921915,0.0001156714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002122603,"about_ca_system_score_gemma":0.0001520238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001808627,"about_ca_topic_score_gemma":0.000002865753,"domain_scores_codex":[0.9965667,0.0005281072,0.001496474,0.0004555129,0.0004544114,0.00049881],"domain_scores_gemma":[0.996265,0.001759106,0.0008016902,0.0003875962,0.0002601551,0.0005264662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003399643,0.0004092733,0.001805132,0.0001115162,0.0001973534,0.0002734067,0.000001641958,0.00005589277,0.00009864701,0.001531072,0.9949555,0.0005264998],"study_design_scores_gemma":[0.0006385047,0.0006604125,0.0006297391,0.0002803957,0.0002819547,0.0005737664,0.0003837492,0.001364734,0.00004842107,4.867358e-7,0.9947775,0.0003603461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002037262,0.001214753,0.992596,0.0005631755,0.0005300638,0.0006107765,0.00009271727,0.00009559956,0.002259701],"genre_scores_gemma":[0.01907304,0.00006541161,0.9796775,0.0001836325,0.0003967405,0.00001373014,0.000006512045,0.00007727254,0.0005061911],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01703577,"threshold_uncertainty_score":0.9999301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232618694315136,"score_gpt":0.3311050290189844,"score_spread":0.2078431595874707,"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."}}