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Estimating endogenous nutrient allocations to reproduction in Redhead Ducks: a dual isotope approach using δD and δ<sup>13</sup>C measurements of female and egg tissues

2004· article· en· W2037063034 on OpenAlexafffund
Keith A. Hobson, Lisa Atwell, Leonard I. Wassenaar, Tina Yerkes

Bibliographic record

VenueFunctional Ecology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change Canada
FundersUniversity of Saskatchewan
KeywordsBiologyYolkAythyaNutrientReproductionWaterfowlPopulationEcologyIncubationAnimal scienceRange (aeronautics)AnatidaeStable isotope ratioIsotopeEndogenyZoologyEndocrinologyBiochemistryHabitat

Abstract

fetched live from OpenAlex

Summary Clutch formation represents a considerable energy expense for waterfowl, yet little evidence is available to quantify nutrient allocation from endogenous and exogenous sources. Here we investigated hydrogen and carbon stable isotope ratios (δD and δ 13 C) in female Redhead Ducks ( Aythya americana ) and their eggs to evaluate the use of δD as an indicator of nutrient sources to reproduction. Females arrived with mean muscle tissue δD and δ 13 C values more positive than those of the local food web, reflecting marine dietary inputs from the wintering grounds. These values changed to the range of local food values by late incubation. δ 13 C values from albumen and yolk protein were correlated, supporting the presence of a common exogenous carbon source for these egg components. There was no significant correlation between δD or δ 13 C values in egg tissues and abdominal fat or muscle from the corresponding laying female. No general population‐level trends in isotope values from sequentially developing follicle yolks were found. Redhead females relied mainly on dietary lipids and proteins for egg production, and therefore endogenous reserves were used to satisfy female body maintenance and energy requirements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.576

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 head0.058
GPT teacher head0.260
Teacher spread0.203 · 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

Citations40
Published2004
Admission routes2
Has abstractyes

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