Short communication: Effects of feeding purple corn (Zea mays L.) silage on productivity and blood superoxide dismutase concentration in lactating cows
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of feeding purple corn (Zea mays L.) silage on productivity and blood superoxide dismutase concentration in lactating cows. We hypothesized that feeding purple corn silage (AX-152; Nagano Animal Industry Experiment Station, Nagano, Japan, and Takii and Co. Ltd., Tokyo, Japan), which is high in anthocyanin content, would increase milk production and blood concentration of superoxide dismutase. We assigned 16 Holstein cows (8 primiparous and 8 multiparous) in mid lactation to 1 of 2 treatments in a randomized block design, with efforts to balance parity, body weight, and days in milk between treatments. Experimental diets contained either purple corn silage [PCS; 31.2% dry matter (DM), 8.4% crude protein, 40.2% neutral detergent fiber, and 26.6% starch] or conventional corn silage (CONT; 30.5% dry matter, 8.7% crude protein, 42.1% neutral detergent fiber, and 26.5% starch) at approximately 32% of diet DM. Both PCS and CONT were ensiled for 5 mo before the study. Treatment diets were fed as total mixed rations ad libitum for 12 wk from February 1 to April 25, 2016. Cows fed the PCS had increased milk yield (31.7 vs. 29.2 kg/d) and blood superoxide dismutase concentrations (9,333 vs. 8,467 U/mL) compared with those fed CONT. However, anthocyanin concentration in the PCS decreased over the 12-wk experiment: 70 mg/kg of DM for the first 4 wk, 20 mg/kg of DM for the second 4 wk, and undetectable for the last 4 wk. We did not detect anthocyanins in the CONT group at any time point. Feeding PCS may increase antioxidant capacity and milk production in dairy cows, but anthocyanin in PCS may be degraded during storage.
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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".