Invasive shrub removal may be more effective at reducing granivory than coating tree seeds with capsaicin
Bibliographic record
Abstract
The utility of seed addition to promote tree regeneration can be greatly limited by animals that consume seeds. Moreover, given that restoration often occurs in forests where invasive shrubs are abundant, and evidence that invasive shrubs can increase granivory, it is important to explore whether methods for reducing granivory work equally well in invaded and uninvaded habitats. We used a multi-site field experiment spanning 160 days to explore whether coating seeds of Prunus serotina with capsaicin extract leads to reduced granivory in habitats with or without invasive shrubs ( Rhamnus cathartica). Capsaicin-coated seeds were removed at a similar rate to uncoated seeds, but seeds in invaded plots had a 78.8% higher rate of removal compared to plots without invasive shrubs. Our findings suggest that managers seeking to encourage regeneration of native trees using direct seeding should consider invasive shrub removal as a top priority to limit the loss of seeds once sown.
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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) | 0.002 | 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".