The influence of herbivores and neighboring plants on risk of browsing: a case study using arctic lupine (<i>Lupinus arcticus</i>) and arctic ground squirrels (<i>Spermophilus parryii plesius</i>)
Bibliographic record
Abstract
We examined how herbivore distribution and density, neighboring plant density and species composition, and individual plant morphology all influence the risk that individual arctic lupines (Lupinus arcticus) will be browsed by arctic ground squirrels (Spermophilus parryii plesius). Risk of being browsed was significantly influenced by the number of resident ground squirrels but not by overall squirrel density at a site. As the leaf density of neighboring conspecifics increased, risk of browsing to an individual lupine decreased except when palatable neighbors were also present. The presence of other palatable species increased the risk of browsing. Risk was highest when both lupine and other palatable neighbors were present. The presence of unpalatable neighbors reduced the risk of browsing of individual lupines. We discuss these results in the context of three hypotheses: (1) attractant decoy, (2) resource concentration, and (3) repellent plant. No single hypothesis accounts for our observations, but an interaction between herbivores, neighbors, and individual lupine morphology determined risk of browsing.
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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 | 0.002 |
| 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 | 0.000 |
| 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".