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Record W2197950776 · doi:10.3791/53476

Milk Collection in the Rat Using Capillary Tubes and Estimation of Milk Fat Content by Creamatocrit

2015· article· en· W2197950776 on OpenAlexafffund
Heather A. Paul, Megan C. Hallam, Raylene A. Reimer

Bibliographic record

VenueJournal of Visualized Experiments · 2015
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Children's Hospital Research InstituteCanadian Institutes of Health ResearchAlberta Innovates
KeywordsOffspringLactationBiologyFood scienceNutrientPhysiologyPregnancy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.448
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2015
Admission routes2
Has abstractyes

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