A new look at energy discounts: using published studies to calculate discounted net energy values for dairy cows fed ad libitum
Bibliographic record
Abstract
Correct assessment of the energetic density of a diet fed to dairy cows is fundamental to accurate prediction of performance. Feeding experiments using lactating Holstein cows fed ad libitum, as reported by North American research facilities in peer-reviewed publications since 1990 in which the actual NEl density of the diet could be calculated from performance, and three dietary energy prediction systems [i.e., NRC (1989), NRC (2001) and an approach developed at the University of California at Davis (UCD)] were used to determine if the NEl discount (i.e., depression) in diets above maintenance (M) energy intake (i.e., 1 ×M) is linear or curvilinear. The principle used was that the expected NEl of the diets could be estimated using the three systems (at 3 ×M ), as well as the NEl calculated from observed performance of the cows, and the deviation (or difference) between the actual and estimated dietary NEl density could be regressed against the actual NEl output (×M) to determine the form of the expected decline in the NEl density of the diet as the energy output (relative to maintenance energy intake) of the cows increased. Results show that output of NEl (×M) is not a predictor of the deviation of the actually calculated NEl level of the diet vs. that estimated by any system. This was unexpected, as all three NEl estimation systems presume a linear decline in the deviation as level of NEl output (×M) increases. In contrast to NEl output (×M), intake of DM (g kg-1 body weight) was a weak predictor (r2 = 0.36 to 0.39 depending on system) of the diet NEl density deviation. The best predictors of diet NEl deviation in all three systems were DM intake (g kg-1BW) and NEl output (×M) combined, where r2 values were much higher (0.76 to 0.79), and there was little evidence of prediction bias in any system. Results suggest that the NEl density of a diet is a function of the inherent characteristics of the diet (i.e., its intrinsic digestibility), its level of consumption, and the genetic merit (and/or stage of lactation ) of the cows that eat it. The lack of a relationship between the deviation of the actually calculated vs. predicted NEl concentration shows that the three NEl prediction equations examined are fundamentally incorrect in their assumption that the NEl concentration of diets declines as the NEl output (×M) of the cows consuming them increases. However, the inaccurate estimates of the actual NEl densities by the systems can be corrected by application of appropriate equations that consider the anticipated level of DM intake and NEl output. Key words: Discount, net energy for lactation
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 | 0.033 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".