Recent advances to improve nitrogen efficiency of grain-finishing cattle in North American and Australian feedlots
Bibliographic record
Abstract
Formulating diets conservatively for minimum crude-protein (CP) requirements and overfeeding nitrogen (N) is commonplace in grain finishing rations in USA, Canada and Australia. Overfeeding N is considered to be a low-cost and low-risk (to cattle production and health) strategy and is becoming more commonplace in the US with the use of high-N ethanol by-products in finishing diets. However, loss of N from feedlot manure in the form of volatilised ammonia and nitrous oxide, and nitrate contamination of water are of significant environmental concern. Thus, there is a need to improve N-use efficiency of beef cattle production and reduce losses of N to the environment. The most effective approach is to lower N intake of animals through precision feeding, and the application of the metabolisable protein system, including its recent updates to estimation of N supply and recycling. Precision feeding of protein needs to account for variations in the production system, e.g. grain type, liveweight, maturity, use of hormonal growth promotants and ß agonists. Opportunities to reduce total N fed to finishing cattle include oscillating supply of dietary CP and reducing supply of CP to better meet cattle requirements (phase feeding).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".