Effect of sodium sulfate concentration in drinking water for beef heifers, and the in vitro effect of bismuth subsalicylate on H<sub>2</sub>S production and fiber disappearance
Bibliographic record
Abstract
This study evaluated the effects of ( i) increasing the concentration of sulfate in drinking water on dry matter intake (DMI), water intake, ruminal fermentation, and apparent total-tract digestibility, and ( ii) water sulfate concentration and bismuth subsalicylate (BSS) dose on in vitro ruminal fermentation. Eight ruminally cannulated beef heifers (382 ± 45 kg) were used in a replicated incomplete 3 × 3 Latin square design. Water treatments contained low (342 ± 29 mg/L; LS), medium (2785 ± 72 mg/L; MS), or high (4948 ± 163 mg/L; HS) sulfate. Ruminal digesta was collected to evaluate water sulfate and BSS dose (0.0%, 0.2%, 0.4%, and 0.6% dry matter) on ruminal H2S production in vitro. Water intake increased linearly as water sulfate concentration increased ( p = 0.002), but DMI was not affected. Heifers drinking MS and HS had greater ruminal H2S at 10.5 h after water provision than LS (sulfate × time, p < 0.001). In vitro H2S production (µg and µg/mL of rumen inoculum) increased and plateaued with increasing sulfate ( p < 0.001) and was linearly reduced ( p < 0.001) by increasing BSS dose. Increasing water sulfate concentrations did not negatively affect water or feed intake but increased ruminal H2S concentrations. Bismuth subsalicylate may reduce H2S production.
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 | 0.000 | 0.001 |
| 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".