Dietary Cation–Anion Difference and Tetany Index of Timothy Forage Fertilized with Liquid Swine Manure
Bibliographic record
Abstract
Incidence of metabolic disorders increases when dairy cows (Bos taurus) are fed forages that have a high dietary cation‐anion difference (DCAD) (>250 mmolc kg−1 dry matter, DM) or high grass tetany (GT) index (>2.2), both associated with high forage K concentration, often caused by applications of liquid swine manure (LSM). We determined how DCAD and GT index of timothy (Phleum pratense L.), grown on two soils with different K availability, were affected by mineral or LSM fertilization. Experimental treatments were: unfertilized control, mineral fertilizer, raw LSM, and liquid fractions of four treated LSM types (decanted, filtered, anaerobically digested, and flocculated), applied in spring and after the first of two harvests each year. Forage DCAD was lowest (−108 mmolc kg−1DM) with the flocculated LSM due to its higher Cl content. Forage DCAD with other LSM types was similar to that with mineral fertilizer. Forage GT indices with LSM and mineral fertilizer were higher than that of the unfertilized control but still within an acceptable range for cows. The DCAD and GT index were greater on soils with high K availability. From spring growth to summer regrowth, these values decreased for soils with low K availability and increased for soils with high K availability. Compared with mineral fertilizer, LSM applied to timothy did not increase the risk of metabolic disorders for dairy cows; a Cl‐enriched LSM can substantially decrease DCAD and lower the risk of milk fever.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".