Validation of 2 urine pH measuring techniques in a prepartum negative dietary cation-anion difference diet and the relationship with production performance
Bibliographic record
Abstract
Negative dietary cation-anion difference (DCAD) diets have been implemented to combat hypocalcemia, a common peripartal disease in dairy cows; however, the extent of compensatory metabolic acidosis necessary and the subsequent effects on performance are still debated. Additionally, there is a need for an inexpensive, accurate method to measure urine pH on farm during the prepartum period to assess the extent of metabolic acidosis achieved by negative DCAD diets. Therefore, this experiment was conducted to determine the accuracy of Fisher pH sticks (pHF; ThermoFisher Scientific) and pHion balance test strips (pHI; pHion Balance) compared with a portable pH meter (pHP; Accumet AP115, ThermoFisher Scientific) in measuring urine pH (UpH) and the effect of UpH on pre- and postpartum dry matter intake (DMI), milk, and milk composition yields. Cows consumed a total mixed ration with a DCAD of -118 mEq/kg for 4 wk prepartum and 397 mEq/kg for 4 wk postpartum. Prepartum UpH measurements (n = 75) for each cow were averaged and used to classify cows in terms of urine pH as low (UpH ≤5.54; mean ± standard deviation; 5.44 ± 0.07), medium (UpH >5.54 and ≤5.90; 5.67 ± 0.09), or high (UpH >5.90; 6.42 ± 0.36). Cows were milked twice a day, and milk samples were taken on d 7 ± 1.3, 14 ± 1.4, and 28 ± 1.1 relative to calving. Milk yield and DMI were recorded daily and averaged weekly. Bland-Altman plots and Lin's concordance correlation coefficient (CCC) were used to assess the agreement between pHP and pHF or pHI (n = 375). Receiver operating characteristic curves were used to determine the threshold with pHF and pHI that best discriminated between UpH >5.75 and ≤5.75 compared with pHP, and area under the curve (AUC) was used to assess the accuracy. At the UpH threshold of 5.75 for pHF and pHI, the sensitivity, specificity, and AUC were 89.5 and 87.4, 99.1 and 97.0, and 0.94 and 0.92, respectively. The CCC was 0.93 for pHF and pHI, indicating near-perfect agreement with pHP. The UpH did not affect pre- or postpartum DMI. There was a tendency for a UpH × week interaction for milk yield, in which milk yields were less for cows in the low and medium groups. In conclusion, pHI and pHF are accurate measurements for UpH, and UpH did not affect DMI; however, when UpH was low or medium, milk yield was decreased at wk 1 postpartum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".