Prediction of nitrogen efficiency in dairy cattle: a review.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Increasing the efficiency of conversion of feed nitrogen (N) into milk and meat N in dairy cattle is an integral part of the effort to maintain or increase food production while decreasing agriculture's environmental impact. Mathematical models provide a means to assess and compare different strategies to increase N efficiency; however, their merit depends on the models' predictive capabilities. Evaluation of the currently available empirical models to predict faecal and urinary N excretion revealed low prediction accuracy and the presence of significant systematic biases. Application of more diverse and advanced model development techniques are needed to produce models whose precision and accuracy are sufficient for application in emissions mitigation protocols. Mechanistic models continue to advance and push the boundaries of knowledge in ruminant N metabolism; aided by advances in computer technology. However, improvement is required in the description of factors that influence microbial protein production and the use of metabolizable protein; this represents the greatest potential for increasing our prediction and understanding of N efficiency in dairy cattle. Attention to these two aspects of ruminant N metabolism in mechanistic models directed specifically at improving N efficiency and in the widely used nutrient requirement models will enhance our ability to meet dairy cattle's protein requirements in a sustainable manner.
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.
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 it