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Record W2098977802 · doi:10.1093/icb/icp048

The evolution of photoperiod response systems and seasonal GnRH plasticity in birds

2009· article· en· W2098977802 on OpenAlexaff
Scott A. MacDougall‐Shackleton, Tyler J. Stevenson, Heather E. Watts, Maria E. Pereyra, Thomas P. Hahn

Bibliographic record

VenueIntegrative and Comparative Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsWestern University
Fundersnot available
KeywordsphotoperiodismBiologyZoologyBotany

Abstract

fetched live from OpenAlex

Animals' lives are typically subdivided into distinct stages, some of which (e.g. breeding) contribute to fitness through enhancing current reproductive success, and some of which (e.g. molting and migration in birds; hibernation in mammals) contribute to fitness through enhancing survival and, therefore, future reproductive opportunities. There is often a trade-off between these two kinds of processes, either because they are temporally incompatible with one another (e.g. migration precludes simultaneous nesting in birds) or because they are energetically incompatible with one another (e.g. successful molting appears to be incompatible with simultaneous nesting in many birds). Consequently, adaptations facilitating appropriate timing and coordination of different life-cycle stages are arguably as important to fitness as are more obvious adaptations such as feeding morphologies and predator avoidance. Mechanisms that facilitate coordination of life-cycle events with the annual cycle of changes in the environment are therefore expected to evolve in response to selection imposed by different environmental challenges. This article focuses on how mechanisms affecting the timing of, and transitions between, life-cycle stages, particularly breeding, have evolved in birds. Through comparative analyses, we show that photorefractoriness and one neuroendocrine correlate of it-plasticity of the gonadotropin releasing hormone system-have evolved in ways that facilitate different degrees of flexibility in timing of the transition from breeding to molting in different environments. We argue that the nature of the mechanistic adaptations will affect the capacity for adaptive adjustments to changing environmental conditions both in the short term (plasticity inherent in individuals) and in the long term (evolutionary responses of populations to selection).

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 score0.055
Threshold uncertainty score0.325

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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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

Citations68
Published2009
Admission routes1
Has abstractyes

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