{"id":"W4413102003","doi":"10.1111/1755-0998.70031","title":"The <scp>CODEX</scp> Approach: High‐Throughput Sequencing of the <scp> <i>Cox</i> </scp> ‐1 Barcode Fragment in Neogastropods (Mollusca, Gastropoda)","year":2025,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"H2020 European Research Council","keywords":"Barcode; Biology; Sanger sequencing; DNA barcoding; DNA sequencing; Computational biology; Massive parallel sequencing; Evolutionary biology; DNA; Genetics; Computer science","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":[0.001584994,0.0003672415,0.0005600517,0.0002400207,0.0008841436,0.00007034986,0.00162232,0.0004699922,0.00009865341],"category_scores_gemma":[0.001131721,0.0002383711,0.0002210768,0.0008305279,0.00155928,0.00008480227,0.0004415332,0.001013059,0.00005491624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005921541,"about_ca_system_score_gemma":0.0003370082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001201395,"about_ca_topic_score_gemma":0.007766534,"domain_scores_codex":[0.99539,0.00152223,0.0006590526,0.0007348307,0.0003722613,0.001321616],"domain_scores_gemma":[0.9966905,0.002019164,0.0002700457,0.0007910033,0.00008876491,0.0001405774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001514971,0.00006572876,0.9835668,0.00004208464,0.0001517489,0.00006122138,0.0004379907,0.01094875,0.0008449673,0.0007753364,0.002131054,0.000959228],"study_design_scores_gemma":[0.0007757798,0.0003313897,0.9727815,0.00002505622,0.00005287569,0.00004145975,0.002281614,0.004767724,0.001733358,0.002180131,0.01497982,0.00004927746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733303,0.001261186,0.0002418207,0.0007586533,0.0004962982,0.0006637814,0.00003014207,0.00004013263,0.02317775],"genre_scores_gemma":[0.9946402,0.0001073791,0.0006986637,0.001095012,0.00007663533,0.0000420392,0.00004232056,0.00001192596,0.003285822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02130997,"threshold_uncertainty_score":0.9720491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007105467882900518,"score_gpt":0.2096145937282108,"score_spread":0.2025091258453103,"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."}}