Short communication: Noninvasive indicators to identify lactating dairy cows with a greater risk of subacute rumen acidosis
Bibliographic record
Abstract
The objective of the current study was to evaluate if milk urea nitrogen (MUN) and milk fat content could be used as the noninvasive indicator to identify cows with greater or lower risk of subacute ruminal acidosis (SARA). Our hypothesis was that cows with lower MUN and milk fat content would have greater risk of SARA, whereas cows with higher MUN and milk fat content would have lower risk of SARA. In the screening study, 35 late-lactating Holstein cows (DIM=250±71.1; BW=601±45.4kg) were fed a high-grain diet containing 35% forage and 65% concentrate mix ad libitum for 21 d. Concentration of MUN ranged from 5.7 to 13.9Mg/dL among the 35 cows, and the average milk fat content was 3.5%. Then, 5 cows with highest MUN concentrations with milk fat higher than 3.5% were selected as animals that presumably have low risk of SARA, and 5 cows with lowest MUN concentrations with milk fat less than 3.5% were selected as animals that presumably have high risk of SARA. These 10 animals were ruminally cannulated during the subsequent dry period. As 1 low-risk cow was culled due to fatty liver, 9 animals (DIM=122±33.2; BW=615±49.1kg) were used in the subsequent study in the following lactation. All cows were fed a high-grain diet consisting of 35% forage and 65% concentrate mix ad libitum for 21 d. Ruminal pH was measured every 30 s for 72 h. Minimum (5.75 vs. 5.30) and mean ruminal pH (6.35 vs. 6.04) was higher for low- compared with high-risk animals. In addition, duration of rumen pH below 5.8 was shorter in low-risk animals (52.5 vs. 395min/d). These results suggested that MUN and milk fat content in late-lactating cows fed a high-grain diet may be used to identify cows that have higher or lower risk of SARA.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".