Evaluation of diagnostic measures for subacute ruminal acidosis in dairy cows
Bibliographic record
Abstract
Li, S., Gozho, G. N., Gakhar, N., Khafipour, E., Krause, D. O. and Plaizier, J. C. 2012. Evaluation of diagnostic measures for subacute ruminal acidosis in dairy cows. Can J. Anim. Sci. 92: 353–364. Effects of subacute ruminal acidosis (SARA) challenges on measurements of feces, urine, milk and blood samples, and of feeding behavior were investigated to determine which of these measurements may aid in the diagnosis of SARA. Eight multiparous lactating dairy cows were used in a crossover design with two 6-wk experimental periods. During weeks 1, 2, and 6, cows received a control diet with a forage-to-concentrate ratio of 58:42. During weeks 3 to wk 5, a grain-based SARA challenge (GBSC) or an alfalfa-pellet SARA challenge (APSC) was conducted by replacing 12% of the dry matter of the control ration with pellets containing 50% ground wheat and 50% ground barley, and by replacing 26% of the dry matter of the control ration with pellets of ground alfalfa, respectively. The rumen pH depression did not differ between the challenges. The GBSC increased the concentrations of lipopolysaccharide (LPS) in feces and of serum amyloid A in blood, but decreased that of milk fat and urea in blood. The APSC increased the urine pH, the net-acid-base excretion, and the red blood cell count and potassium concentration in blood. Both challenges increased the concentrations of LPS and propionate in rumen fluid, protein in milk, glucose, lactate and sodium and the partial pressure of CO 2 in blood, and tended to decrease the concentration of chloride in blood. The measures that were similarly affected by both challenges may aid in the diagnosis of a rumen pH depression. Differences between the SARA challenges suggest that this disorder is not solely rumen pH dependent.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".