MétaCan
Menu
Back to cohort
Record W2105374118 · doi:10.1603/en11188

Climate Change and Temperate Zone Insects: The Tyranny of Thermodynamics Meets the World of Limited Resources

2012· article· en· W2105374118 on OpenAlexafffund
Shelley A. Adamo, Jillian Baker, Maggie M. E. Lovett, Graham Wilson

Bibliographic record

VenueEnvironmental Entomology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsTemperate climateClimate changeBiologyPopulationEcologyPopulation growthDemography

Abstract

fetched live from OpenAlex

Climate change will result in warmer temperatures and an increase in the frequency and severity of extreme weather events. Given that higher temperatures increase the reproductive rate of temperate zone insects, insect population growth rates are predicted to increase in the temperate zone in response to climate. This consensus, however, rests on the assumption that food is freely available. However, under conditions of limited food, the reproductive output of the Texan cricket Gryllus texensis (Cade and Otte) was highest at its current normal average temperature and declined with increasing temperature. Moreover, low food availability decreased survival during a simulated heat wave. Therefore, the effects of climate change on this species, and possibly on many others, are likely to hinge on food availability. Extrapolation from our data suggests that G. texensis will show larger yearly fluctuations in population size as climate change continues, and this will also have ecological repercussions. Only those temperate zone insects with a ready supply of food (e.g., agricultural pests) are likely to experience the predicted increase in population growth in response to climate change; food-limited species are likely to experience a population decline.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.229
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental EntomologySame topicSpecies Distribution and Climate ChangeFrench-language works237,207