Egg Protein as a Source of Power, Strength, and Energy
Bibliographic record
Abstract
In Brief High-quality proteins make a valuable contribution to the synthesis and maintenance of muscle and indirectly to the regulation of blood glucose levels, thus contributing to power, strength, and energy. Eggs have traditionally been used as the standard of comparison for measuring protein quality because of their essential amino acid (EAA) profile and high digestibility. They provide a nutrient-dense source of energy from protein and fat, approximately 75 kcal per large egg, as well as several B vitamins, including thiamin, riboflavin, folate, B12, and B6, which are required for the production of energy by the body. Given the unique complementary relationship between the EAA leucine and glucose utilization by muscle, it would follow that a diet rich in the amino acid leucine would be advantageous to men and women undergoing endurance training. Leucine is also a critical element in regulating muscle protein synthesis and may be the key amino acid defining the increased needs for EAA to optimize skeletal muscle mass. Increased tissue levels of leucine combine with circulating insulin to allow skeletal muscles to manage protein metabolism and fuel selection in relation to diet composition. Moreover, muscle recovery from exercise, both resistance and endurance, seems to be dependent on dietary leucine. Because eggs are an excellent, nutrient-rich source of leucine, as well as other EAAs, these protein-related benefits may be important to active individuals who routinely consume eggs as part of a varied, balanced diet Do eggs deserve a second look?
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".