Profiling of Bacillus cereus on Canadian grain
Bibliographic record
Abstract
Microorganisms that cause foodborne illnesses challenge the food industry; however, environmental studies of these microorganisms on raw grain, prior to food processing, are uncommon. Bacillus cereus sensu lato is a diverse group of bacteria that is common in our everyday environment and occupy a wide array of niches. While some of these bacteria are beneficial to agriculture due to their entomopathogenic properties, others can cause foodborne illness; therefore, characterization of these bacteria is important from both agricultural and food safety standpoints. We performed a survey of wheat and flax grain samples in 2018 (n = 508) and 2017 (n = 636) and discovered that B. cereus was present in the majority of grain samples, as 56.3% and 85.2%, in two years respectively. Whole genome sequencing and comparative genomics of 109 presumptive B. cereus isolates indicates that most of the isolates were closely related and formed two genetically distinct groups. Comparisons to the available genomes of reference strains suggested that the members of these two groups are not closely related to strains previously reported to cause foodborne illness. From the same data set, another, genetically more diverse group of B. cereus was inferred, which had varying levels of similarity to previously reported strains that caused disease. Genomic analysis and PCR amplification of genes linked to toxin production indicated that most of the isolates carry the genes nheA and hbID, while other toxin genes and gene clusters, such as ces, were infrequent. This report of B. cereus on grain from Canada is the first of its kind and demonstrates the value of surveillance of bacteria naturally associated with raw agricultural commodities such as cereal grain and oilseeds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".