Enhancing predictions of nitrogen excretion in beef cattle in the tropics
Bibliographic record
Abstract
Context Excreted fecal and urinary N can cause environmental contamination. Aims Our objective was to evaluate models for predicting nitrogen (N) excreted in feces (FN), urine (UN) and manure of beef cattle; and to update the Nutrient Requirements of Zebu and Crossbred Cattle Committee dataset, and develop new models for predicting FN, UN and N in manure. Methods The dataset consisted of 30 works published between 1999 and 2024, including bulls, steers and heifers, with Nellore and crossbred animals. Criteria for inclusion in the dataset included studies in which the cattle production system was designed for meat, and the availability of individual animal data for model development and evaluation. Key results To estimate FN (g/day), two equations were adjusted, a multiple regression considering nitrogen intake (NI; g/day) and bodyweight (kg) as independent variables (concordance correlation coefficient (CCC) = 0.55; root mean square error of prediction (RMSEP) = 32.8%; RMSEP:observations standard deviation ratio (RSR) = 0.96), and a simple linear regression with NI as the independent variable (CCC = 0.53; RMSEP = 33.4%; RSR = 0.97). To predict UN (g/day), an exponential model was adjusted from NI (CCC = 0.65; RMSEP = 26.1%; RSR = 0.73). Regarding N excretion in manure (g/day), an exponential model was also used with NI as a predictor variable (CCC = 0.84; RMSEP = 15.6%; RSR = 0.52). The intercept and slope of the relationship between predicted and observed values for all developed equations were similar to 0 and 1 (P ≥ 0.09). Conclusions The equations generated were robust and accurate in estimating N excretion by feedlot beef cattle. Implications These models will provide support for planning production systems and reducing N excretion into the environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".