Effects of mechanical site preparation and slash removal on long-term productivity of conifer plantations in Sweden
Bibliographic record
Abstract
Mechanical site preparation is commonly used to increase survival and early growth of newly planted seedlings. Ideally, any early positive effects of site preparation should persist for a long time, but concerns have been raised as to whether intensive site preparation might have a negative effect on the long-term productivity of a stand. The present study was therefore designed to investigate the long-term effects of different site-preparation methods on productivity and determine any possible interactions with tree species and site fertility. In the 1980s, a randomized block experiment was established at sites throughout Sweden. Four site-preparation methods of various intensities were performed on different sites: (i) an untreated control, (ii) disc trenching, (iii) mounding, and (iv) ploughing. As a complementary treatment, slash was either retained or removed from some plots. Depending on soil moisture class, geographical position, and site index, Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), or lodgepole pine (Pinus contorta Douglas ex Loudon) were planted in pure stands. Growth variables such as height and diameter were measured during the first years after establishment. After about 30 years, these variables were remeasured at the level of individual trees. Overall, an increased production of the planted trees after site preparation was found. Neither intensive site preparation (such as ploughing) nor slash removal had any negative effect on the long-term productivity of these experimental stands.
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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 | 0.001 |
| 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 | 0.001 | 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".