Temperature and food quality effects on growth, consumption and post-ingestive utilization ef.ciencies of the forest tent caterpillar <i>Malacosoma disstria</i> (Lepidoptera: Lasiocampidae)
Bibliographic record
Abstract
Temperature and food quality can both influence growth rates, consumption rates, utilization efficiencies and developmental time of herbivorous insects. Gravimetric analyses were conducted during two consecutive years to assess the effects of temperature and food quality on fourth instar larvae of the forest tent caterpillar Malacosoma disstria Hübner. Larvae were reared in the laboratory at three different temperatures (18, 24 and 30 degrees C) and on two types of diet; leaves of sugar maple trees Acer saccharum Marsh. located at the forest edge (sun-exposed leaves) or within the forest interior (shade-exposed leaves). In general, larvae reared at 18 degrees C had lower growth rates and lower consumption rates than larvae reared at the warmer temperatures (24 and 30 degrees C). Moreover, the duration of the instar decreased significantly with increasing temperatures. Type of diet also affected the growth rates and amount of food ingested by larvae but did not affect the duration of the instar. Larvae fed sun-exposed leaves consumed more food and gained higher biomasses. Values of approximate digestibility and efficiency of conversion of ingested food were also higher when larvae were fed sun-exposed leaves. Higher growth rates with increasing temperatures were primarily the result of the shorter stadium duration. The higher growth rates of larvae fed sun-exposed leaves were possibly the result of stimulatory feeding and consequently greater food intake and also a more efficient use of food ingested. This study suggests that the performance of M. disstria caterpillars could be enhanced by warmer temperatures and higher leaf quality.
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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".