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Record W2005904771 · doi:10.4039/n05-076

How do primary nutrients affect the performance and preference of forest tent caterpillars on trembling aspen?

2006· article· en· W2005904771 on OpenAlexafffund
Meghan K. Noseworthy, Emma Despland

Bibliographic record

VenueThe Canadian Entomologist · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyHerbivoreCaterpillarNutrientSalicaceaeLepidoptera genitaliaSugarBotanyHorticultureCafeteriaHost (biology)Woody plantEcologyFood science

Abstract

fetched live from OpenAlex

Abstract Variation in leaf quality includes differences in both primary nutrients and secondary metabolites. Both of these factors can influence the feeding preference and resulting performance of herbivores in ways that are difficult to disentangle when comparing foliage from different sources. Our study was designed to assess the effects of the ratio of the primary nutrients in host-tree foliage, protein and sugar, on the performance and feeding behaviour of the forest tent caterpillar ( Malacosoma disstria Hübner (Lepidoptera: Lasiocampidae)). Fourth-stadium larvae were fed trembling aspen leaves ( Populus tremuloides Michx (Salicaceae)) supplemented with casein, sucrose, or buffer only (control). No differences in taste responses to the three leaf types were detected. In a cafeteria situation, feeding behaviour over the short term was largely determined by the use of pheromone trails and hence depended on which leaf was contacted first. Over the longer term, caterpillars fed most on the control leaf and the sugar-supplemented leaf and discriminated against the protein-supplemented leaf. Sugar supplementation increased survivorship relative to the control treatment but slowed development and did not affect growth; protein supplementation decreased growth. These findings are consistent with past research comparing forest tent caterpillar performance and feeding preference on different host plants.

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 score0.497
Threshold uncertainty score0.901

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 studies0.0000.001
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 head0.015
GPT teacher head0.193
Teacher spread0.178 · 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

Citations16
Published2006
Admission routes2
Has abstractyes

Explore more

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