D<scp>IFFERENT</scp> A<scp>PPROACHES TO</scp> D<scp>EFINE</scp> I<scp>NDIVIDUAL</scp> A<scp>MINO</scp> A<scp>CID</scp> R<scp>EQUIREMENTS</scp>
Bibliographic record
Abstract
A full review of the strengths and limitations of the various methods used to define amino acid requirements is provided. The focus is on the recent development of carbon oxidation techniques such as indicator amino acid oxidation and 24-h amino acid balance to determine dietary indispensable (essential) amino acid needs in adults. All approaches depend on the change in a metabolic parameter in response to graded intake of the test amino acid. In humans, the within-subject variance is less than the between-subject variance, which has led to an appreciation of the need to study each subject across a range of intakes, above and below the mean requirement level. The data can then be analyzed using two-phase linear regression crossover and a precise population mean requirement can be determined. Several approaches have been used to define the variance of the mean requirement. Finally, a minimally invasive indicator amino acid oxidation model has been developed which allows the determination of dietary essential amino acid requirements in children and other vulnerable populations.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.029 |
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".