An Expert Consensus Framework for a Database on Ileal Amino Acid Digestibility and Protein Quality Scoring From Foods Consumed by Humans
Bibliographic record
Abstract
Objectives: Food Protein Quality is best measured by the amino acid scoring that relates their indispensable amino acid content to a reference profile after correction for digestibility. Digestibility is measured at the terminal ileum for calculation of the ileal Protein Digestibility Corrected Amino Acid Score (ileal-PDCAAS) and the Digestible Indispensable Amino Acid Score (DIAAS). In 2022, the Food and Agriculture Organisation (FAO) and the International Atomic Energy Agency (IAEA) decided to create a database on amino acid oro-ileal digestibility for protein quality scoring of human foods.
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 | 0.121 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".