Bibliographic record
Abstract
The dynamics of protein turnover confer great advantages for homeothermy, plasticity and metabolic function in mammals. The different roles played by the various organs have led to aspects of protein synthesis and degradation that aid the various functions performed. The so-called “non-productive” organs such as the gastro-intestinal tract and liver produce large quantities of export proteins that perform vital functions. Not all these proteins are recovered, however, and thus function can result in lowered net conversion of plant protein to animal products. The splanchnic tissues also oxidize essential amino acids (AA). For example, the gut catabolizes leucine, lysine and methionine, but not threonine and phenylalanine, as part of a complex interaction between AA supply and tissue metabolic activity. Losses by oxidation and endogenous secretions can markedly alter the pattern of absorbed AA. The fractional rates of extraction of total AA inflow to the liver are low and this allows short-term flexibility in controlling supply to peripheral tissues. Recent evidence suggests that the role of the liver in AA catabolism is more a response to non-use by other tissues rather than an immediate regulation of supply to the periphery. Neither arterial supply of AA nor the rate of transport into peripheral tissues limits protein gain, except when supply is very limited. Rather, control is probably exerted via hormone-nutrient interactions. Key words: Protein synthesis, amino acid, gastro-intestinal tract, liver, muscle, mammary gland
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".