Application of the law of diminishing returns to estimate maintenance requirement for amino acids and their efficiency of utilization for accretion in young chicks
Bibliographic record
Abstract
SUMMARY Suitability of the monomolecular equation, specifically re-parameterized for analysing energy balance data, has recently been investigated in broilers and turkeys. In the current study, this equation was applied to literature data from growing chicks fed crystalline amino acid (AA) diets, in order to provide estimates for AA requirements for maintenance, body-weight gain and protein accretion. Non-linear regression was used with the data to estimate parameters and combine them to determine other biological indicators. The predictive ability of the model was evaluated with reference to model behaviour when fitting the data, biologically meaningful parameter estimates and statistical performance. The model estimated the maintenance requirements for valine, threonine and lysine to be in the range 80–111, 96–109 and 52–209 mg/kg of liveweight/day, respectively, depending on the response criterion. Requirements for maintenance were in good agreement with values reported previously. Average efficiency of recovering AAs in whole body protein, between maintenance and four×maintenance, was in the reported range of 0·80–1·0 and greatest at low intakes and decreasing as intakes increase.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".