Foliage‐age mixing within balsam fir increases the fitness of a generalist caterpillar
Bibliographic record
Abstract
Abstract. 1. Manipulative field studies were carried out to evaluate the foliage age preference–performance relationship for an extreme generalist herbivore, the whitemarked tussock moth ( Orygia leucostigma Smith) (Lepidoptera: Lymantriidae), within balsam fir [ Abies balsamea (L.) Mill]. 2. Field surveys indicated that early instar caterpillars fed almost exclusively on young (i.e. current‐year) foliage, whereas late instars caterpillars fed on both young and mature (i.e. 1‐ and 2‐year‐old) foliage. 3. Survival of early instar caterpillars was highest in treatments where current‐year and/or 1‐year old foliage were available, but decreased significantly on older foliage. In contrast, late instar caterpillars had the highest survival when allowed to feed on all age classes of foliage, whereas potential fecundity was highest for late instars that fed on young foliage. 4. Overall, caterpillars had 32–65% higher fitness when able to feed on all rather than just one age class of foliage. 5. These results support both the ‘complementary diet' hypothesis, which states that dietary mixing of different‐aged foliage can increase nutrient uptake and/or dilute harmful secondary plant chemicals, and the ‘ontogeny’ hypothesis, which attributes changes in diet to changes in the nutritional needs and/or tolerance to plant defences of juvenile insects as they develop.
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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) | 0.002 | 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".