Take Me to Your Leader: Does Early Successional Nonhost Vegetation Spatially Inhibit<i>Pissodes strobi</i>(Coleoptera: Curculionidae)?
Bibliographic record
Abstract
The spatial influences of host and nonhost trees and shrubs on the colonization patterns of white pine weevil Pissodes strobi (Peck) were studied within a stand of planted interior hybrid spruce [Picea glauca (Moench) Voss x Picea engelmannii (Parry) ex Engelm.]. Planted spruce accounted for one third of all trees within the stand, whereas the remaining two thirds were comprised of early-successional nonhost vegetation, such as alder (Alnus spp.), paper birch (Betula papyrifera Marsh.), black cottonwood [Populus balsamifera ssp. trichocarpa (T. Ng.) Brayshaw], lodgepole pine [Pinus contorta (Dougl.) ex Loud.], trembling aspen (Populus tremuloides Michx), willow (Salix spp.), and Canadian buffaloberry [Shepherdia canadensis (L.) Nutt.]. Unlike the spruce trees, nonhost vegetation in the stand was not uniformly distributed. Spatial point process models showed that Canadian buffaloberry, paper birch, black cottonwoood, and trembling aspen had negative associations with damage caused by the weevil, even though the density of the insects' hosts in these areas did not change. Moreover, knowing the locations of these nonhost trees provided as much, or more, inference about the locations of weevil-attacked trees as knowing the locations of suitable or preferred host trees (i.e., those larger in size). Nonhost volatiles, the alteration of soil composition, and overstory shade are discussed as potential explanatory factors for the patterns observed. New research avenues are suggested to determine whether nonhost vegetation in early successional stands might be an additional tool in the management of these insects in commercially important forests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".