Utilization of milk amino acids for body gain in suckling mink ( <b> <i>Mustela vison</i> </b> ) kits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The efficiency of utilization of milk amino acids for body gain in suckling mink kits from small (n = 3), medium (n = 6) and large litters (n = 9) was investigated by using 36 mink dams and their litters for measurements during lactation weeks 1 through 4. Measurements on each dam and litter were performed once, hence three dams per litter size each week (n = 9). Individual milk intake of kits was determined, milk samples were collected and kits were killed for determination of amino acid composition. The most abundant amino acids in milk were glutamate, leucine and aspartate making up about 40% of total amino acids. Branched chained amino acids made up slightly more than 20% and sulphur containing amino acids less than 5% of total milk amino acids. In kit bodies the sum of glutamate, aspartate and leucine made up about 32% of amino acids, branched chain amino acids about 16% and sulphur containing amino acids about 4%. The amino acid composition of both milk and bodies changed as lactation progressed with decreasing proportions of essential amino acids. The ratio between body and milk amino acids was constantly over 1 only for lysine, suggesting that it was the most limiting amino acid in mink milk. Milk amino acids were efficiently utilized during week 1, ranging from 74.7% (lysine) to 42.1% (leucine), with an average for essential amino acids of 58.4%. Tendencies for improved utilization of lysine (74.7-78.2%), phenylalanine (61.0-70.0%), histidine (62.4-68.8%), arginine (61.3-70.4%) and all essential amino acids (58.4-60.2%) from week 1 to week 2 were recorded. During weeks 3 and 4, the efficiency declined, and for all essential amino acids the average utilization was 38.1% during week 4.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it