Bibliographic record
Abstract
Abstract Nutritional assessment of the quality of a protein source is a complex process with different requirements depending on the jurisdiction being discussed. The Protein Efficiency Ratio (PER) is a growth measurement required in Canada, whereas the United States uses the Protein Digestibility Corrected Amino Acid Score (PDCAAS), which requires the determination of the amino acid profile and digestibility of the protein source. Notably both PER and PDCAAS are in vivo methods, necessitating the use of animals for measurement of growth and protein digestibility. Understandably there is significant interest from both industry and consumers to reduce reliance on animal experimentation for content claims. It is notable that outside of North America there are multiple examples of in vitro protein quality assessment. In Europe protein content claims are based on whether the protein content contributes >12% of the total energy, and in Australia content claims begin at 5 g of protein per serving. As the use of in vitro techniques for protein quality assessment has not led to indices of protein malnutrition in Europe and Australia, should be possible to use in vitro techniques in methods such as PER and PDCAAS thereby removing the reliance on animal use for quality assessment in North America. This can be accomplished by using in vitro methods in the assessment of protein digestibility and relying on recent changes by Health Canada to allow for calculation of PER from PDCAAS values. This presentation will introduce how protein quality is calculated via PER/PDCAAS, and subsequently discuss modifications of these methods to incorporate in vitro techniques to generate values indicating their nutritional quality with positive correlations to in vivo data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".