{"id":"W4409197617","doi":"10.1016/j.atech.2025.100923","title":"Harnessing machine learning for grain mycotoxin detection","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Millar College of the Bible; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mycotoxin; Computer science; Artificial intelligence; Machine learning; Biology; Biotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002020578,0.0002953149,0.0003344965,0.00007479418,0.0007697,0.00009229089,0.0004094887,0.0004830651,0.00007588509],"category_scores_gemma":[0.000225568,0.00009913495,0.0002030126,0.001645136,0.00008941143,0.0001424121,0.0001455305,0.0004446488,0.00003600772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007235016,"about_ca_system_score_gemma":0.000006255231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006758748,"about_ca_topic_score_gemma":0.001589539,"domain_scores_codex":[0.9983947,0.00006038691,0.0003472262,0.0005348613,0.0001344475,0.0005283934],"domain_scores_gemma":[0.9993826,0.0001567379,0.0001425977,0.00008070883,0.0001790813,0.00005825363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002813497,0.00005869126,0.002787248,0.00001542582,0.00003806761,0.000001389791,0.00001710942,0.000003634291,0.8363528,0.003100905,0.001510729,0.1560859],"study_design_scores_gemma":[0.0005797124,0.0006379386,0.06536717,0.00009386253,0.00007806454,0.00005881208,0.001067021,0.0002135679,0.3448133,0.006296114,0.5801721,0.000622293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846362,0.001136828,0.0003771301,0.006981577,0.0007533424,0.0009432413,0.00002725001,0.001124832,0.004019573],"genre_scores_gemma":[0.9940089,0.00005616191,0.0003905117,0.0001976453,0.0002341098,0.0002879488,0.0001952729,0.000001611181,0.004627838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5786614,"threshold_uncertainty_score":0.5919988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00867268580086841,"score_gpt":0.2162773339908312,"score_spread":0.2076046481899627,"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."}}