Application of strong ion difference theory to urine and the relationship between urine pH and net acid excretion in cattle
Bibliographic record
Abstract
OBJECTIVE: To develop an equation expressing urine pH in terms of independent variables, derive an equation relating urine pH to net acid excretion (NAE), and apply this new knowledge to determine the role that monitoring urine pH should play when diets with low cationanion difference are fed to dairy cattle. ANIMALS: 11 Holstein-Friesian cows. PROCEDURES: A physicochemical strong ion approach was used to develop a general electroneutrality equation for urine that involved urine pH and strong ion difference (SID [difference between strong cation and strong anion concentrations]), PCO(2), the concentration of ammonium ([NH(4)(+)]) and phosphate ([PO(4)]), and 3 constants. The general electroneutrality equation was simplified for use in bovine urine and applied to 321 data points from 11 cows fed different diets. RESULTS: Urine pH was dependent on 4 independent variables (urine SID, [NH(4)(+)], PCO(2), and [PO(4)]) and 3 constants. The simplified electroneutrality equation for bovine urine was pH approximately {pK(1)' - log(10)(S PCO(2))} + log(10)([K(+)] + [Na(+)] + [Mg(2+)] + [Ca(2+)] + [NH(4)(+)] - [Cl(-)] - [SO(4)(2-)]). The relationship between urine pH and NAE (in mEq/L) for cattle fed different diets was pH = 6.12 + log(10)(-NAE + [NH(4)(+)] + 2.6). CONCLUSIONS AND CLINICAL RELEVANCE: A change in urine SID, [NH(4)(+)], PCO(2), or [PO(4)] independently and directly led to a change in urine pH. Urinary [K(+)] had the greatest effect on urine pH in cattle, with high urine [K(+)] resulting in alkaline urine and low urine [K(+)] resulting in acidic urine. Urine pH provided an accurate assessment of NAE in cattle when pH was > 6.3.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".