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Record W4413987089 · doi:10.1093/icb/icaf143

Identifying the Physiological Mechanisms That Underlie Phenotypic Responses to Rapid Environmental Change

2025· article· en· W4413987089 on OpenAlexaff
Britt J. Heidinger, Jennifer L. Grindstaff, Gabrielle Names, Jill T. Anderson, George A. Brusch, Lauren B. Buckley, Amanda S. Cicchino, Morgan W. Kelly, Eric A. Riddell, Conor C. Taff

Bibliographic record

VenueIntegrative and Comparative Biology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersSociety for Integrative and Comparative BiologyDivision of Integrative Organismal SystemsDivision of Biological InfrastructureSICB Division of Animal Behavior
KeywordsPhenotypeBiologyEnvironmental changeEvolutionary biologyNeuroscienceEcologyGeneticsClimate changeGene

Abstract

fetched live from OpenAlex

Synopsis In response to rapidly changing environmental conditions, many organisms are experiencing shifts in geographic ranges and in the timing and expression of key life-history traits, which have important effects on fitness. However, the physiological mechanisms that mediate these phenotypic responses, such as endocrine and other signaling pathways are not well understood. This information will be critical for predicting organismal responses to climate change because physiological mechanisms are often highly responsive to environmental cues and influence the phenotypic variation available to selection. Additionally, they often integrate suites of correlated traits and are thus expected to influence the evolutionary response to selection. The overarching goals of this symposium were to gain novel insights into the physiological mechanisms that underlie organismal responses to rapidly changing environmental conditions and to identify gaps in knowledge and experimental approaches to advance the field. Here we review and discuss the symposium contributions and the research themes that emerged as important foci for future studies.

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 score0.824
Threshold uncertainty score0.656

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)0.0010.000

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 head0.167
GPT teacher head0.331
Teacher spread0.164 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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