Utilization of milk amino acids for body gain in suckling mink ( <b> <i>Mustela vison</i> </b> ) kits
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.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".