Corrigendum to “Characterizing ruminal acidosis risk: A multiherd, multicountry study” (J. Dairy Sci. 106:3155–3175)
Bibliographic record
Abstract
In the Results and Discussion section of this article, 2 values were inverted. The 2 corrected numbers are marked in boldface below. There were 261 cows from 32 farms classified into 1 of the 3 acidosis risk groups, 26.1% of the cows classified as high risk, 26.8% as medium risk, and 47.1% as low risk for acidosis (Table 3). The authors regret the error. Characterizing ruminal acidosis risk: A multiherd, multicountry studyJournal of Dairy ScienceVol. 106Issue 5PreviewA multicenter observational study was conducted on early lactation Holstein cows (n = 261) from 32 herds from 3 regions (Australia, AU; California, CA; and Canada, CAN) to characterize their risk of acidosis into 3 groups (high, medium, or low) using a discriminant analysis model previously developed. Diets ranged from pasture supplemented with concentrates to total mixed ration (nonfiber carbohydrates = 17 to 47 and neutral detergent fiber = 27 to 58% of dry matter). Rumen fluid samples were collected <3 h after feeding and analyzed for pH, and ammonia, d- and l-lactate, and volatile fatty acid (VFA) concentrations. Full-Text PDF Open Access
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.153 | 0.055 |
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".