Mycotoxin determination in fungal contaminated Canadian silage toxic to dairy cows and goats
Bibliographic record
Abstract
Silage has become a key component of year-long animal feed in Canada and parts of northern Europe. It provides several advantages to farmers over traditional feed components, such as increased digestibility, higher nutrient content and preservation of the forages to meet seasonal feeding demands. Some ensiled materials can contain toxic fungal metabolites resulting from ‘in field’ contamination. In addition, when improperly stored or exposed to air during the feedout stage, silage is highly susceptible to aerobic spoilage by yeasts and filamentous fungi resulting in lower nutrient value and further mycotoxin contamination. In this study, silage samples were collected from 25 Canadian dairy goat and cattle farms where animals experienced feed-related health issues. Twenty-six unique fungal species were isolated from these samples, with the majority being Penicillium . High resolution liquid chromatography tandem mass spectrometry (HRLC-MS/MS) was used to identify a total of 125 known mycotoxins and fungal secondary metabolites from these silage samples, many of which were not produced by the 26 isolated filamentous fungi grown in agar cultures. Various mycotoxins resulting from preharvest contamination were detected, including ergot alkaloids, fumonisins and trichothecenes, some in high concentrations. Toxins produced after harvest included roquefortine C, citrinin and penitrem A. This study reinforces the need for farmers to implement best management practices to minimise fungal contamination and the resulting mycotoxin deposition in their crop and stored feed to maintain animal health.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".