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Record W2124639839 · doi:10.1242/jeb.051540

The role of the gut in insect chilling injury: cold-induced disruption of osmoregulation in the fall field cricket,<i>Gryllus pennsylvanicus</i>

2011· article· en· W2124639839 on OpenAlexafffund
Heath A. MacMillan, Brent J. Sinclair

Bibliographic record

VenueJournal of Experimental Biology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHemolymphBiologyOsmoregulationInsectHomeostasisField cricketCricketZoologyEcologyEndocrinologySalinity

Abstract

fetched live from OpenAlex

To predict the effects of changing climates on insect distribution and abundance, a clear understanding of the mechanisms that underlie critical thermal limits is required. In insects, the loss of muscle function and onset of cold-induced injury has previously been correlated with a loss of muscle resting potential. To determine the cause of this loss of function, we measured the effects of cold exposure on ion and water homeostasis in muscle tissue, hemolymph and the alimentary canal of the fall field cricket, Gryllus pennsylvanicus, during an exposure to 0°C that caused chilling injury and death. Low temperature exposure had little effect on muscle osmotic balance but it dissipated muscle ion equilibrium potentials through interactions between the hemolymph and gut. Hemolymph volume declined by 84% during cold exposure whereas gut water content rose in a comparable manner. This rise in water content was driven by a failure to maintain osmotic equilibrium across the gut wall, which resulted in considerable migration of Na(+), Ca(2+) and Mg(2+) into the alimentary canal during cold exposure. This loss of homeostasis is likely to be a primary mechanism driving the cold-induced loss of muscle excitability and progression of chilling injury in chill-susceptible insect species.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.210

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 science0.0010.000
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 head0.050
GPT teacher head0.322
Teacher spread0.272 · 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

Citations183
Published2011
Admission routes2
Has abstractyes

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