Milk Collection in the Rat Using Capillary Tubes and Estimation of Milk Fat Content by Creamatocrit
Bibliographic record
Abstract
Milk, as the sole source of nutrition for the newborn mammal, provides the necessary nutrients and energy for offspring growth and development. It also contains a vast number of bioactive compounds that greatly affect the development of the neonate. The analysis of milk components will help elucidate key factors that link maternal metabolism and health with offspring growth and development. The laboratory rat represents a popular model organism for maternal studies, and rat milk can be used to examine the effect of various maternal physiological, nutritional, and pharmacological interventions on milk components, which may then impact offspring health. Here a simple method of manually collecting milk from the lactating rat that can be performed by a single investigator, does not require specialized vacuum or suction equipment, and provides sufficient milk for subsequent downstream analysis is described. A method for estimating the fat content of milk by measuring the percentage of cream within the milk sample, known as the creamatocrit, is also presented. These methods can ultimately be used to increase insight into maternal-child health and to elucidate maternal factors that are involved in proper growth and development of offspring.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".