143 Effect of Supplementary Selenium Source on Dairy Cow Performance, Antioxidant Status, and Apparent Absorption and Retention
Bibliographic record
Abstract
Abstract The objective of this experiment was to determine how source of selenium (Se) affects dairy cow performance, antioxidant status, and apparent absorption and retention. Multiparous Holstein cows (n = 24; 597 ± 49 kg BW) were blocked by days in milk (161 ± 18) and randomly assigned to receive 0.3 mg/kg of either: 1) organic Se (selenized yeast; ORG; Sel-Plex-Alltech); or, 2) inorganic Se (sodium selenite; INO) premix, top dressed and mixed into a ration daily. After an 11-week adaptation period (blood and milk sampled monthly), cows received an intraruminal dose of Se77 (either Se77 yeast or NaSe77O3) followed by a 4-day period of blood and rumen fluid sampling, and total collection of feces, urine, and milk. Data were analyzed using PROC GLIMMIX in SAS with fixed effects of treatment, time, and their interaction, and random effect of block. Daily DMI (23 ± 0.6 kg), milk yield (35 ± 1.2 kg), plasma glutathione peroxidase (64 ± 4.2 U), and serum Se (0.11 ± 0.003 µg/g) were not different between treatments during the adaptation period. Serum Se77 maximum concentration and area under the curve (AUC) were not different between treatments for 72 hours following infusion, but rumen fluid Se77 AUC was higher (P = 0.02) for ORG cows. Apparent absorption (64 ± 1.3%) and retention (44 ± 1.5%) of the Se77 dose did not differ between treatments. The ORG cows had lower urinary excretion (13 vs. 17 ± 0.6%; P < 0.01), higher milk excretion (6 vs. 2 ± 0.3%; P < 0.01), and similar fecal excretion (36 ± 1.3%; P = 0.9) of Se77 compared to INO cows. These results indicate that ORG Se increased Se content of milk and decreased Se excretion in urine, but did not alter performance, antioxidant status, and apparent absorption and retention of Se in mid-lactation cows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".