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Record W2136838293 · doi:10.4039/n05-802

Insect adaptations to cold and changing environments

2006· article· en· W2136838293 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Canadian Entomologist · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsCanadian Museum of Nature
Fundersnot available
KeywordsPredictabilityHardiness (plants)DiapauseContext (archaeology)EcologyBiologyEnvironmental changeAdaptation (eye)Climate changeNeuroscience

Abstract

fetched live from OpenAlex

Abstract A review of insect adaptations for resistance to cold and for life-cycle timing reveals the complexity of the adaptations and their relationships to features of the environment. Cold hardiness is a complex and dynamic state that differs widely among species. Surviving cold depends on habitat choice, relationships with ice and water, and synthesis of a variety of cryoprotectant molecules. Many aspects are time-dependent and are integrated with other factors such as taxonomic affinity, resource availability, natural enemies, and diapause. Timing adaptations reflect the fact that all environments change over many different time frames, from days to thousands of years. Environments differ in severity and in the extent, nature, variability, and predictability of change, as well as in how reliably cues indicate probable conditions in the future. These differences are reflected by a wide range of insect life-cycle systems, life-cycle delays, levels of responsiveness to various environmental signals, genetic systems, and circadian responses. In particular, the degree of environmental change, its predictability on different time frames, and whether it can be monitored effectively dictate the balance between fixed and flexible timing responses. These same environmental features have to be characterized to understand cold hardiness, but this has not yet been done. Therefore, the following key questions must be answered in order to put cold hardiness into the necessary ecological context: How much do conditions change? How consistent is the change? How reliable are environmental signals?

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.

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 score0.923
Threshold uncertainty score0.788

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 head0.021
GPT teacher head0.204
Teacher spread0.183 · 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