Bibliographic record
Abstract
Human infection with Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) is relatively rare, but the consequences can be serious, especially in the very young and the elderly. Efforts to control the flow of STEC O157 during beef processing have meaningfully reduced the incidence of human STEC O157 infection, particularly prior to 2005. Unfortunately, despite early progress, the incidence of STEC O157 infection has not changed meaningfully or statistically in recent years, suggesting that additional actions, for example, targeting the cattle reservoir, are necessary to further reduce STEC O157 illness. Ideally, preharvest interventions against STEC O157 should reduce the likelihood that cattle carry the organism, have practical application within the beef production system, and add sufficient value to the cattle to offset the cost of the intervention. A number of STEC O157 antigens are being investigated as potential vaccine targets. Some vaccine products have demonstrated efficacy to reduce the prevalence of cattle carrying STEC O157 by making the gut unfavorable to colonization. However, in conditions of natural exposure, efficacy afforded by vaccination depends on how the products are used to control environmental transmission within groups of cattle and throughout the production system. Although cattle vaccines against STEC O157 have gained either full or preliminary regulatory approval in Canada and the United States, widespread use by cattle feeders is unlikely until there is an economic signal to indicate that cattle vaccinated against STEC O157 are valued over other cattle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".