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Record W2147929240 · doi:10.1242/jeb.066381

Metabolism and energy supply below the critical thermal minimum of a chill-susceptible insect

2012· article· en· W2147929240 on OpenAlexaff
Heath A. MacMillan, Caroline M. Williams, James F. Staples, Brent J. Sinclair

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsWestern University
Fundersnot available
KeywordsInsectEnergy metabolismThermalEnvironmental scienceBiologyEcologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

When exposed to temperatures below their critical thermal minimum (CT(min)), insects enter chill-coma and accumulate chilling injuries. While the critical thermal limits of water-breathing marine animals may be caused by oxygen- and capacity-limitation of thermal tolerance (OCLT), the mechanisms are poorly understood in air-breathing terrestrial insects. We used thermolimit respirometry to characterize entry into chill-coma in a laboratory population of fall field crickets (Gryllus pennsylvanicus). To detect potential oxygen limitation, we quantified muscle ATP, lactate and alanine concentrations in crickets following prolonged exposure to 0°C (a temperature that causes chill-coma, chilling injury and eventual death). Although there was a sharp (44%) drop in the rate of CO(2) emission at the CT(min) and spiracular control was lost, there was a low, continuous rate of CO(2) release throughout chill-coma, indicating that the spiracles were open and gas exchange could occur through the tracheal system. Prolonged exposure to 0°C caused muscle ATP levels to increase marginally (rather than decrease as OCLT would predict), and there was no change in muscle lactate or alanine concentration. Thus, it appears that insects are not susceptible to OCLT at low temperatures but that the CT(min) may instead be set by temperature effects on whole-animal ion homeostasis.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.661

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)0.0010.000

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 head0.015
GPT teacher head0.256
Teacher spread0.240 · 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

Citations53
Published2012
Admission routes1
Has abstractyes

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