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Record W2038657864 · doi:10.2298/abs1203109j

Effects of temperature and dietary nitrogen on genetic variation and covariation in gypsy moth larval performance traits

2012· article· en· W2038657864 on OpenAlexaff
Milena Janković‐Tomanić, Jelica Lazarević

Bibliographic record

VenueArchives of Biological Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsGypsy mothLymantria disparHeritabilityLarvaBiologyPhenotypic plasticityInstarGenetic variationZoologyDry weightEcologyEvolutionary biologyBotanyGenetics

Abstract

fetched live from OpenAlex

To assess the plastic and genetic components of variation in responses of gypsy moth (Lymantria dispar) 4th instar larvae to temperature and food quality, we applied a split-family four-environment experimental design where full-sibs were reared on two constant temperatures (23?C and 28?C) and two concentrations of dietary nitrogen (1.5 and 3.7% dry weight). A temperature of 28?C and low dietary nitrogen decreased larval weight and prolonged larval developmental time, while viability was not affected. Only a marginally significant interaction between the two environmental factors was found for larval weight. The broad-sense heritability for larval developmental time did not change across environments, and across-environment genetic correlations were close to one. Heritability for larval weight depended on environmental and across-environmental genetic correlations that were not significant. There was no evidence of a trade-off between developmental time and larval weight. The implications of the obtained results for the evolution of phenotypic plasticity in complex environments are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 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

Citations10
Published2012
Admission routes1
Has abstractyes

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