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Record W1963994839 · doi:10.1086/673260

Juvenile Nutritional Stress Affects Growth Rate, Adult Organ Mass, and Innate Immune Function in Zebra Finches (<i>Taeniopygia guttata</i>)

2013· article· en· W1963994839 on OpenAlexaff
Buddhamas Kriengwatana, Haruka Wada, Alexander Macmillan, Scott A. MacDougall‐Shackleton

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersUniversity of Chicago
KeywordsTaeniopygiaJuvenileBiologyImmune systemInnate immune systemZebra finchZoologyPhysiologyImmunologyEcologyNeuroscience

Abstract

fetched live from OpenAlex

Developmental conditions may influence many aspects of adult phenotype, including growth and immune function. Whether poor developmental environments impair both growth and immune function or induce a trade-off between the two processes is inconclusive, and the impact of the timing of stress in determining this relationship has so far been overlooked. We tested the hypothesis that the long-term effects of nutritional stress on growth, body composition, and immune function in zebra finches (Taeniopygia guttata) are different depending on whether stress is experienced during an early or a juvenile phase (i.e., before or after nutritional independence, respectively). We raised birds on high (H) or low (L) food conditions until posthatch day (PHD) 35 and switched treatments for half of the birds in each of the H and L groups from PHD 36 to 61. We found that unfavorable juvenile conditions (PHD 36-61) increased somatic growth rates and liver mass, body fat, and some aspects of immune function. We also observed a positive relationship between growth and immune function, as individuals that grew faster as juveniles also had better innate immune responses as adults. There was no effect of treatment on basal metabolic rate. These findings demonstrate the importance of juvenile developmental conditions in shaping multiple aspects of the adult phenotype.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.362

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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