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Record W2102315483 · doi:10.1139/z08-042

Carbon- and nitrogen-isotope tissue–diet discrimination and turnover rates in deer mice, Peromyscus maniculatus

2008· article· en· W2102315483 on OpenAlexaffvenue
Johnston F. Miller, John S. Millar, Fred J. Longstaffe

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsWestern University
Fundersnot available
KeywordsPeromyscusBiologyDeer mouseMuscle tissueJuvenileAnimal scienceTurnoverIsotopeIsotopes of nitrogenStable isotope ratioEndocrinologyInternal medicineZoologyEcology

Abstract

fetched live from OpenAlex

The dietary habits of most small mammals are not well documented, and stable isotope measurements can provide information on when and how diets change. Here we document the discrimination and turnover times for carbon and nitrogen isotopes in blood, liver, muscle, hair, and milk from deer mice ( Peromyscus maniculatus (Wagner, 1845)) fed a controlled diet. Nonbreeding adults and pregnant females were livetrapped, maintained on a commercial laboratory chow, and had tissues sampled on a regular schedule. After adjusting to the laboratory diet, most tissues of nonbreeding adults were slightly depleted of 13 C and enriched in 15 N relative to diet (Δ 13 C tissue–diet range = 0.3‰ to –1.1 ‰; Δ 15 N tissue–diet range = 1.9‰ to 3.4‰). Liver (half-lives of 2.8 and 3.6 days for C and N, respectively) turned over more rapidly than blood (22.4 and 19.8 days for C and N, respectively) and muscle (18.7 and 24.8 days for C and N, respectively). The isotopic compositions of nonbreeding and breeding adults indicated tissue turnover at approximately the same rate, but juvenile tissues reflected the laboratory diet much more quickly than adult tissues.

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)0.000
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 score0.430
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations69
Published2008
Admission routes2
Has abstractyes

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