Vegetative growth response of black cherry (<i>Prunus serotina</i>) to different mechanical control methods in a biosphere reserve
Bibliographic record
Abstract
We assessed the effectiveness of different management strategies against the non-native invasive tree species black cherry ( Prunus serotina Ehrh.). The species causes substantial management problems in European forest ecosystems, like the Valle del Ticino Biosphere Reserve in Italy, by suppressing the regeneration of native tree species. This can modify ecological key processes and cause biodiversity loss. Since chemical and biological control has mainly been abandoned in European forest ecosystem management, mechanical control measures are presently the preferred option to proceed against the black cherry but have shown very limited results in the reserve. The aim was to control the success of felling the species and to test other mechanical control methods such as girdling and snapping the trees with regard to their efficiency by quantifying the species’ growth reactions. For this purpose, observational studies were conducted in two forest stands of which one was treated in 1996 and the other more recently in 2009. A subsample of resprouting stumps was treated a second time in 2010 to observe the effect of a direct second cutback. An experimental study was implemented in a third forest stand also in 2010 to compare three different mechanical control methods. The results suggest that felling black cherry is ineffective if the objective is to reduce the species’ abundance because resprouts occur on 100% of the treated trees and biomass increment is not reduced in the long term. Girdling proved to be the most effective treatment across the diameter classes considered.
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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) | 0.001 | 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".