Fusarium Damage in Small Cereal Grains from Western\nCanada. 2. Occurrence of Fusarium Toxins\nand Their Source Organisms in Durum Wheat Harvested in 2010
Bibliographic record
Abstract
Samples\nof Canadian western amber durum harvested in 2010 were\nobtained as part of the Canadian Grain Commission Harvest Sample Program,\ninspected, and graded according to Canadian guidelines. A subset of Fusarium-damaged samples were analyzed for Fusarium species as well as mycotoxins associated\nwith these species, including deoxynivalenol and other trichothecenes,\nmoniliformin, enniatins, and beauvericin. Overall, Fusarium avenaceum and F. graminearum were the top two most frequently recovered species. Phaeosphaeria nodorum (a.k.a. Septoria\nnodorum), F. culmorum, F. poae, F. acuminatum, and F. sporotrichioides were observed\nin samples as well. All samples analyzed for mycotoxins contained\nquantifiable concentrations of enniatins, whereas beauvericin, deoxynivalenol,\nand moniliformin were measured in approximately 75% of the samples.\nConcentrations in Fusarium-damaged\nsamples ranged from 0.011 to 34.2 mg/kg of enniatins plus beauvericin,\nup to 4.7 mg/kg of deoxynivalenol, and up to 6.36 mg/kg of moniliformin.\nComparisons of enniatins, beauvericin, and moniliformin concentrations\nto the occurrence of various Fusarium species suggest the existence of an infection threshold above which\nthese emerging mycotoxins are present at higher concentrations. The\ncurrent grading factor of Fusarium-damaged\nkernels manages concentrations of these emerging mycotoxins in grain;\nlower provisional grades were assigned to samples that contained the\nhighest concentrations of enniatins, beauvericin, and moniliformin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".