Are Super-Shedder Feedlot Cattle Really Super?
Bibliographic record
Abstract
The objective of this study was to determine the frequency and duration of super-shedding in cattle by enumerating Escherichia coli O157:H7 in feces and to compare lineage and pulsed-field gel electrophoresis (PFGE) subtypes from super- and low-shedders. E. coli O157:H7 was enumerated from fecal samples obtained from the rectums of 400 feedlot cattle. Super-shedding steers (N=11) were identified, transported, and penned individually. Freshly voided fecal pats were sampled 2 h before and 6 h after feeding for 7 d, then once daily for an additional 19 d. Isolates (N=126) were subtyped using PFGE, and lineage was typed using a lineage-specific polymorphism assay. Of the 11 super-shedders identified at the commercial feedlot, only five were confirmed as super-shedders at the research feedlot, with no super-shedders identified 6 d after sampling at the commercial feedlot. Super-shedding was not consistent in fecal pats collected from the same individual at different times of the day. Isolates exhibited three distinct PFGE subtypes, with most isolates (97.6%) displaying the same subtype, including those obtained from steers that transitioned from super- to low-shedding. The short duration of super-shedding and its lack of continuance suggest that these individuals may not play as great a role in the dissemination of E. coli O157:H7 within the feedlot as previously proposed.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".