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Record W2346292624 · doi:10.1093/cz/zow049

The effect of fasting and body reserves on cold tolerance in 2 pit-building insect predators

2016· article· en· W2346292624 on OpenAlexaff
Inon Scharf, Alma Daniel, Heath A. MacMillan, Noa Katz

Bibliographic record

VenueCurrent Zoology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsYork University
FundersEuropean Commission
KeywordsBiologyPredationAbiotic componentCold toleranceInsectFreezing toleranceEcologyStarvationCold stressZoologyBotanyGeneGeneticsEndocrinology

Abstract

fetched live from OpenAlex

Pit-building antlions and wormlions are 2 distantly-related insect species, whose larvae construct pits in loose soil to trap small arthropod prey. This convergent evolution of natural histories has led to additional similarities in their natural history and ecology, and thus, these 2 species encounter similar abiotic stress (such as periodic starvation) in their natural habitat. Here, we measured the cold tolerance of the 2 species and examined whether recent feeding or food deprivation, as well as body composition (body mass and lipid content) and condition (quantified as mass-to-size residuals) affect their cold tolerance. In contrast to other insects, in which food deprivation either enhanced or impaired cold tolerance, prolonged fasting had no effect on the cold tolerance of either species, which had similar cold tolerance. The 2 species differed, however, in how cold tolerance related to body mass and lipid content: although body mass was positively correlated with the wormlion cold tolerance, lipid content was a more reliable predictor of cold tolerance in the antlions. Cold tolerance also underwent greater change with ontogeny in wormlions than in antlions. We discuss possible reasons for this lack of effect of food deprivation on both species' cold tolerance, such as their high starvation tolerance (being sit-and-wait predators).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.113

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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