Damage by insect herbivores on white spruce in plantation and natural understory regeneration
Bibliographic record
Abstract
Few studies focused on non-outbreaking herbivorous insects to understand the patterns of damage they inflict on plants. We compared damage by herbivorous insects on young white spruce (Picea glauca) between natural regrowth in the understory of mixed wood forest and small extensively-managed plantations. We observed damage to foliage to quantify damage by different groups of herbivores, including leaf chewers, miners and sap-sucking species. Our hypothesis stated that trees in forest understory environments would have higher diversity of damages caused by insects but that plantation trees would have more damaged tree shoots. Our two sampling methods were branch collection, in which we collected a forty-centimeter branch and recorded foliar damage, and field surveys, where one researcher recorded foliar damage on the saplings for three-minute intervals. We also measured tree growth, canopy openness, soil temperature and humidity. We used these environmental variables in general linear models to test their effects on herbivore damage in the two habitats. The results showed that plantation and understory trees did not differ significantly in the overall amount of insect damage. There was no correlation found with any environmental factor. This pattern indicated that the plantation we sampled maintained insect biodiversity similar to that in mixed wood forests. Thus, small, extensively managed multispecies plantations can be less at risk of insect outbreaks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".