DYNAMICS OF WILD RED RASPBERRY (RUBUS IDAEUS L.) AND THE INFLUENCE ON TREE REGENERATION WITHIN SILVICULTURAL OPENINGS IN A NORTHERN HARDWOOD STAND
Bibliographic record
Abstract
Previous studies have investigated how the abundance of raspberries (Rubus idaeus L.) impacts tree regeneration, but few have linked these impacts to location within canopy openings with a legacy tree. To fill this knowledge gap, we investigated the presence, abundance, and location of raspberries within openings containing legacy trees and the resulting impacts on tree regeneration. During the winter of 2003, 49 openings were created of three sizes: small, medium, large and 20 reference single-tree selection sites in a northern hardwood stand in Ford Forest near Alberta, Michigan. Tree regeneration and vegetative species cover were recorded in 2005 and were re-sampled in 2016. Results show raspberries not only persisted, but increased throughout the time-period. In addition, raspberry abundance varied by location within the openings. Furthermore, high abundance of raspberries was shown to decrease total tree seedling count. However, high abundance of raspberry did not hinder the growth of saplings within treatment areas. These results provide silviculturists beneficial information when deciding if raspberry control is necessary.
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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 | 0.001 | 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".