Invited Review: Ethanol co-products for dairy cows: there goes our starch … now what?
Bibliographic record
Abstract
Paz, H. A., Castillo-Lopez, E., Ramirez-Ramirez, H. A., Christensen, D. A., Klopfenstein, T. J. and Kononoff, P. J. 2013. Invited Review: Ethanol co-products for dairy cows: there goes our starch … now what? Can. J. Anim. Sci. 93: 407–425. The rise of the grain-ethanol industry has resulted in a dramatic increase in the availability of feed co-products namely, distillers’ grains with solubles (DG). The cost of feeds that have traditionally been used for energy continues to increase and there is a need to understand the potential impact of replacing these feeds with DG on milk yield and composition. Using the Dairy National Research Council (2001) model and data from a total of 25 published studies (81 observations), we used a meta-analytical procedure to evaluate the impact of feeding corn milling co-products on lactating dairy cows. Based on our model, the inclusion of DG did not affect dry matter intake (P=0.35) but a trend for an increase in milk yield was observed (P=0. 10). Additionally, the percentages of milk fat and protein in the milk were not affected (P=0.24 and 0.25, respectively). The modeled daily rumen outflow of Lys in grams was significantly (P<0.01) reduced and Met was not affected (P=0.79) when DG were included. The error associated with net energy of lactation allowable milk averaged 5.0±1.0 kg d−1and was not affected by DG (P=0.79) but the error associated with metabolizable protein allowable milk tended (P=0.10) to be higher for cows consuming DG by 1.4±1.2 kg d−1.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".