Regeneration of Mediterranean Pinus sylvestris under two alternative shelterwood systems within a multiscale framework
Bibliographic record
Abstract
The inability to obtain sufficient numbers of naturally regenerated trees following partial harvests of some Mediterranean Basin managed forests has prompted the need to critically assess common silvicultural practices. In this study, we examined Scots pine ( Pinus sylvestris L.) regeneration patterns under two shelterwood systems using a multiscale framework. The uniform shelterwood (US) system includes heavier and less frequent timber extractions than the group shelterwood (GS) system. Removal of competing vegetation to expose mineral soil (soil preparation) is sometimes used for US but is not commonly needed in GS. A generalized linear model was used to predict regeneration density for each shelterwood system using environmental variables at microsite- and forest-level scales, medium-scale overstory tree characteristics, and spatial metrics that represent a range of spatial scales. Although US had a higher mean regeneration density, GS had a wider range of regeneration ages. The results derived from this study suggest that ground-level disturbance to break up the herb or organic layer may be required for regeneration establishment. This may occur during repeated partial harvests; otherwise, soil preparation may be required. Overall, this multiscale framework approach resulted in improved predictions and a better understanding of regeneration processes.
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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) | 0.000 | 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".