{"id":"W4413963077","doi":"10.1111/2041-210x.70140","title":"Optimizing image capture for computer vision‐powered taxonomic identification and trait recognition of biodiversity specimens","year":2025,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Guelph; Vector Institute","funders":"Office of Advanced Cyberinfrastructure; Natural Sciences and Engineering Research Council of Canada; National Institute of Food and Agriculture; Arkansas NSF EPSCoR; Battelle; Office of International Science and Engineering; U.S. Department of Agriculture; Office of Experimental Program to Stimulate Competitive Research; National Science Foundation","keywords":"Biodiversity; Trait; Identification (biology); Artificial intelligence; Taxonomic rank; Biology; Pattern recognition (psychology); Computer science; Computer vision; Ecology; Taxon","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":[0.03395062,0.001034692,0.0008828617,0.00536014,0.001269895,0.004425982,0.004367594,0.001865078,0.00652777],"category_scores_gemma":[0.05023172,0.0009607176,0.001115035,0.003087525,0.002212788,0.005189041,0.004540994,0.001567506,0.00339129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001896479,"about_ca_system_score_gemma":0.004054921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002760807,"about_ca_topic_score_gemma":0.006314104,"domain_scores_codex":[0.9830983,0.007463943,0.001655814,0.001669147,0.00553717,0.0005754979],"domain_scores_gemma":[0.9565762,0.01608103,0.00339841,0.007114944,0.01624534,0.0005840914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003247295,0.0002038182,0.01535525,0.005777367,0.0002757211,0.0002752209,0.001523496,0.009972766,0.1404156,0.04259356,0.03206835,0.7512141],"study_design_scores_gemma":[0.0001451424,0.001102453,0.04785066,0.00690981,0.0006672266,0.002088329,0.00356513,0.05054339,0.4370965,0.07095861,0.3784864,0.0005864006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0194452,0.003454882,0.9577344,0.003076087,0.0002829104,0.001159275,0.0009243808,0.002674602,0.01124821],"genre_scores_gemma":[0.06248647,0.003119117,0.9281814,0.000936345,0.00009876848,0.001622798,0.00118374,0.0005410926,0.001830272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03395062,"threshold_uncertainty_score":0.1795502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03222701735270504,"score_gpt":0.3141453723850346,"score_spread":0.2819183550323296,"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."}}