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Record W2148714255 · doi:10.1139/z04-033

Metabolism and life-history correlates in a lowland and highland population of a terrestrial isopod

2004· article· en· W2148714255 on OpenAlexvenueno aff
Marco A. Lardies, Tamara P. Catalán, Francisco Bozinovic

Bibliographic record

VenueCanadian Journal of Zoology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEctothermFecundityPopulationLife history theoryEcologyPopulation dynamicsZoologyHabitatLife historyDemography

Abstract

fetched live from OpenAlex

Ectotherms distributed along environmental gradients often show marked variation in physiological and life-history traits. Different life-history phenotypes may be correlated with variations in maintenance metabolism. We measured life-history traits and metabolic rate (VO 2 ) of the common wood louse (Porcellio laevis Latreille, 1804) from a lowland population and a highland population in northern Chile. We measured VO 2 at 5, 12, 18, and 25 °C. Wood lice from different altitudes exhibited large variations in life-history traits, which were correlated with changes in VO 2 . Fecundity of wood lice decreased and egg volume, offspring size, and reproductive output of females increased in highland populations compared with lowland populations. Isopods from the highland had a higher VO 2 than those from lowland habitats for almost all temperatures. However, when we considered VO 2 at 5 °C, VO 2 was lowest in the high-altitude isopods. In addition, since the highest thermal sensitivity values in the VO 2 –temperature curves were found at the lowest and highest temperature ranges for the highland population rather than for the lowland population, our study did not support the metabolic cold-adaptation hypothesis. We concluded that differences in VO 2 between the populations contributed to the difference in reproductive output by wood lice from cold and warm habitats.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.972

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Citations64
Published2004
Admission routes1
Has abstractyes

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