Recent advances to improve nitrogen efficiency of grain-finishing cattle in North American and Australian feedlots
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
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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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 | 0.002 |
| 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