How do primary nutrients affect the performance and preference of forest tent caterpillars on trembling aspen?
Bibliographic record
Abstract
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 disstriaHübner (Lepidoptera: Lasiocampidae)). Fourth-stadium larvae were fed trembling aspen leaves (Populus tremuloidesMichx (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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".