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Record W2130577304 · doi:10.1644/1-mamm-a-078.1

Milk composition in a hibernating rodent, the Columbian ground squirrel (<i>Urocitellus columbianus</i>)

2013· article· en· W2130577304 on OpenAlexfundno aff
Amy L. Skibiel, Wendy R. Hood

Bibliographic record

VenueJournal of Mammalogy · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
FundersAuburn UniversityUniversity of CalgarySmithsonian InstitutionNational Science Foundation
KeywordsLactationHibernation (computing)Ground squirrelOffspringWeaningBiologyRodentCalciumAnimal scienceComposition (language)ZoologyEcologyChemistryPregnancySquirrel monkey

Abstract

fetched live from OpenAlex

Milk is essential to a mammalian mother's reproductive strategy and is necessary for offspring growth and development. In hibernators with a short duration between weaning and winter immergence, milk synthesis is likely constrained by time and trade-offs between maternal and offspring condition, thus influencing milk composition. We characterized the proximate and mineral composition of milk produced by a hibernating rodent, the Columbian ground squirrel (Urocitellus columbianus). The concentration of all milk components varied across lactation; the concentration of most constituents peaked between days 14 and 19 postpartum. Columbian ground squirrel milk was relatively low in lipids but high in protein and calcium. At peak lactation, milk was composed of 10.71% ± 0.46% SE protein, 9.15% ± 0.47% lipids, 3.39% ± 0.13% sugar, and 0.47% ± 0.02% calcium (wet mass basis). High protein, energy from protein, and calcium in milk corroborate earlier reports of the importance of fast growth rates of juveniles to overwinter survival, whereas the low lipid content of milk may reflect fat conservation for adults. Production of high-calcium milk also may be a preventive mechanism enabling offspring to cope with bone mineral loss during hibernation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.897

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)0.0010.000

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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 designObservational
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

Citations9
Published2013
Admission routes1
Has abstractyes

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