Shiga toxin-producing Escherichia coli in the feces of Alberta feedlot cattle.
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) are a public health concern. Bacterial culture techniques commonly used to detect E. coli O157:H7 will not detect other STEC serotypes. Feces from cattle and other animals are a source of O157:H7 and other pathogenic serotypes of STEC. The objective of this study was to estimate the pen-level prevalence of Shiga toxins and selected STEC serotypes in pre-slaughter feedlot cattle. Composite fecal samples were cultured and a polymerase chain reaction (PCR) was used to detect genes for Shiga toxins (stx1 and stx2) and genes for O157:H7, O111:H8, and O26:H11 serotypes. Evidence of Shiga toxins was found in 23 pens (92%), O157:H7 in 2 (8%), O111:H8 in 5 (20%), and O26:H11 in 20 (80%) of the 25 pens investigated. Although pen-level prevalence estimates for Shiga toxins and non-O157 serotypes seem high relative to O157:H7, further effort is required to determine the human health significance of non-O157 serotypes of STEC in feedlot cattle.
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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 | 0.001 |
| 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".