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Record W2060272128 · doi:10.1242/jeb.021642

THE COST OF EATING GRASS AND GROWING FAST

2009· article· en· W2060272128 on OpenAlexaff
Charles Darveau

Bibliographic record

VenueJournal of Experimental Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiologyEcologyBasal metabolic rateVariation (astronomy)Zoology

Abstract

fetched live from OpenAlex

The metabolic cost of maintaining living tissue is expressed by a measurement used routinely in animal biology; the basal metabolic rate (BMR). Despite the extensive use of this measurement, our understanding of how and why this rate varies among species, populations or individuals remains elusive. The difficulties that researchers face in disentangling variation in BMR are in part due to the challenge of identifying the source of such variation. Complex physiological traits vary depending on the species and its lifestyle, the environment an individual lives in, the conditions experienced during development, and the genetic make-up of an individual. The interface between the fields of physiology and evolutionary biology now allows for experimental approaches designed to quantify the contribution of genetic and environmental variables to the variation of complex physiological traits such as BMR. This approach was recently used by Edyta Sadowska, Pawel Koteja and their colleagues from Jagiellonian University in Poland, where they tested the hypothesis that BMR is genetically correlated with growth rate and the ability to cope with a low-quality grass diet in a mammalian species: the bank vole.The team established a laboratory colony of bank voles, which allowed them to carry out a quantitative genetics study. The team were interested in testing the genetic correlation between growth rate, the ability to cope with a low-quality grass diet, and BMR. First the team captured voles in the field,and then raised six subsequent generations of the colony in the laboratory. They measured the growth rate of the voles by recording the rodents' body mass change over the 10 day period after the youngsters were weaned. To assess how individuals cope with low food quality, they fed individuals on a grass diet for 4 days and measured the change in body mass. Finally, the team measured each individual's BMR by assessing the rate of oxygen consumption.At weaning, the voles weighed on average 10 g (21 days), with their body masses ranging from around 6–15 g. During the 10 d following weaning,individuals gained an average of 5 g, varying from approximately 2 to 9 g. After being fed a low-quality grass-based diet for 4 days, the rodents lost an average of 1.3 g in body mass, ranging from a 1 g mass gained to a 4 g mass loss.The breeding experiment enabled the researchers to estimate which components of the observed variation among individuals were due to genetic or environmental factors. They determined that postweaning growth rate and body mass change when fed a grass diet are heritable traits, where some of the variation in the trait is genetically derived. This finding adds to Sadowska and Koteja's previous work showing that variation in mass-independent BMR is also heritable. The authors then showed a genetic correlation between postweaning growth rate, mass change on a grass diet and BMR. This last piece of information suggests that if selection acts on increasing growth rate or the ability to cope with a low-quality grass diet, then an increase in BMR would also evolve, as they are genetically correlated.By measuring the variation of complex physiological traits in individual voles over multiple generations, the group was able not only to assess the nature versus nurture components of these traits, but also to show that a life-history trait such as growth rate and an ecological trait such as food habits can influence the evolution of BMR.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes1
Has abstractyes

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