Larval diet prior to and following virus ingestion influences the efficacy of two nucleopolyhedroviruses in whitemarked tussock moth (<i>Orgyia leucostigma</i>) caterpillars
Bibliographic record
Abstract
Abstract Food quality can influence the performance of immature insects and their interactions with pathogens, such as viruses. In manipulative field studies, virus-free caterpillars of the whitemarked tussock moth (WMTM) (Orgyia leucostigma (Smith)) had higher survival, more female-biased sex ratios, and were larger when feeding on white birch (Betula papyrifera Marshall) versus balsam fir (Abies balsamea (Linnaeus) Miller) or red spruce (Picea rubens Sargent). Subsequent laboratory studies with two nucleopolyhedroviruses, derived from WMTMs and Douglas-fir tussock moths, indicated that caterpillars fed high quality food (i.e., artificial diet) prior to infection had less mortality associated with virus infection than those feeding on lower quality foliage (i.e., birch). In field studies, caterpillars fed birch following infection had significantly lower mortality than those feeding on relatively lower quality foliage (i.e., balsam fir). We postulate that higher nutritional quality in artificial diet relative to birch (previrus-ingestion nutrition) and in birch relative to balsam fir foliage (postvirus-ingestion nutrition) has a positive effect on the ability of tussock moth caterpillars to resist or recover from viral infections, although the specific mechanisms responsible for observed resistance remain unclear.
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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".