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Record W2529341060 · doi:10.1139/cjz-2016-0108

Effect of nutritional stress and sex on melanotic encapsulation rate in the sexually size dimorphic Cook Strait giant weta (<i>Deinacrida</i><i> rugosa</i>)

2016· article· en· W2529341060 on OpenAlexafffundvenue
Clint D. Kelly

Bibliographic record

VenueCanadian Journal of Zoology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySexual dimorphismSexual selectionImmune systemEffectorZoologyRugosaSex characteristicsIntraspecific competitionEcologyImmunologyEndocrinologyBotany

Abstract

fetched live from OpenAlex

Nutritional condition and sex are known to influence efficacy and investment in immune function. A poor diet is costly to immune function because it limits the resources (e.g., protein) available to effector systems (e.g., melanotic encapsulation), whereas males and females are expected to differ in how they allocate resources to fitness-related traits. Males are expected to invest less in immunity, and more in mating, than females, but this pattern could be reversed if fitness is more condition-dependent in males than in females. I tested the effects of nutritional condition and sex on melanotic encapsulation rate in the Cook Strait giant weta (Deinacrida rugosa Buller, 1871), an orthopteran insect exhibiting strong female-biased sexual size dimorphism that is, at least in part, the result of strong sexual selection for small male size. I found that male D. rugosa have a stronger encapsulation response than females, while nutritional condition has only a small positive effect on this particular effector system in both sexes. Whether the observed sex difference in encapsulation ability is due to a physiological constraint in females or whether males allocate more resources to this effector system because their fitness is more condition-dependent than female’s remains to be determined.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.318
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations8
Published2016
Admission routes3
Has abstractyes

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