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Record W2153284619 · doi:10.1525/auk.2009.08197

Influence of Diet on Egg Size in American Coots (<i>Fulica americana</i>): Evidence from Food Supplementation and Biochemical Markers

2009· article· en· W2153284619 on OpenAlexafffundabout
Gary R. Bortolotti, Keith A. Hobson, Usne J. Butt

Bibliographic record

VenueThe Auk · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsBiologyYolkCarotenoidZoologyFood qualityNutrientEcologyAnimal scienceFood science

Abstract

fetched live from OpenAlex

Food-supplementation studies on birds during egg laying have generally manipulated macronutrients and energy. Such studies have shown variable effects on egg size in American Coots (Fulica americana). We determined the relative influences of local environment, food quantity, and food quality on egg size in American Coots by supplementing food and carotenoids at three sites in Saskatchewan. Eggs were collected and analyzed for carotenoid content and stable isotopes (δ15N and δ13C) in the yolk to determine whether variation in the type of food eaten contributes to egg size. We also report on the isotopic analysis of American Coot tissues and their eggs to assess evidence of endogenous versus exogenous protein and lipid allocations to reproduction. We provide isotopic evidence that American Coots used endogenous lipid, but not protein, reserves for egg formation and that egg size is more dependent on exogenous sources of nutrients. Laying sequence was the only variable across nesting locales that had a significant influence on the size of eggs. There was an effect of provisioning food at only one site; however, this response was driven by a component of diet quality other than carotenoids. Yolk δ13C values were positively correlated with egg volume, which further suggests the importance of diet quality in determining egg size in American Coots.

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 score0.080
Threshold uncertainty score0.278

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 studies0.0000.001
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

Citations4
Published2009
Admission routes3
Has abstractyes

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