PSVIII-6 Impact of Naturally Occurring Water with High Sulfate Concentration on Feed and Water Intake, Growth, and Trace Mineral Status of Beef Cattle
Bibliographic record
Abstract
Abstract This study evaluated the impact of naturally occurring water with a high-sulfate concentration on dry matter intake (DMI), water intake, growth, and trace mineral status of yearling beef heifers. Angus-based heifers (n = 48; 398.4 ± 22.1 kg) were randomly assigned to 1 of 12 pens (4 heifers/pen) and fed for 100 d arranged into 25 d periods. Heifers were provided water containing 350 (CON); 2,300 (MEDS); or 4,300 mg/L (HIGHS) sulfate. Concentrated water (71,482 mg/L sulfate) from natural source was collected and blended to achieve the treatments. All data were analyzed using the fixed effects of treatment, period (d 1-25, 26-50, 51-75, and 76-100), and the treatment × period interaction, with period as a repeated measure. Average daily gain, and gain-to-feed were not affected (P>0.29). Provision of HIGHS decreased (interaction, P< 0.01) DMI from d 1-25, 26-50 and 51-75, with no effect from d 76-100 compared with CON. Heifers drinking CON and MEDS during d 26-50 had greater (interaction, P< 0.01) water intake than HIGHS with no differences among treatments otherwise. Total sulfate intake (g/d) was greater for HIGHS, followed by MEDS and CON with a greater difference between treatments during d 26-50 (interaction, P< 0.01). Heifers provided MEDS and HIGHS had a greater (interaction, P< 0.01) reduction in serum copper compared with CON. Liver copper concentration did not differ at the start of the study but was 74% and 62% of CON for MEDS and HIGHS, respectively at the end of the study (interaction, P< 0.01). Serum selenium was greater for MEDS and HIGHS than CON; while liver selenium was least for HIGHS heifers at the end of the study (interaction, P< 0.01). These data highlight that beef heifers were able to tolerate 213 g/d of sulfate intake with no negative impact on growth performance; however, MEDS and HIGHS decreased liver copper and liver selenium concentrations.
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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".