En jämförelse på beståndsnivå kring snö och vindskador hos Pinus contorta latifolia och Pinus sylvestris i norra Sverige
Bibliographic record
Abstract
Pinus Contorta was introduced to Sweden as a tree that would grow fast with a promising future, also a promise in increased production. What could not be anticipated was the common Swedish weather conditions, such as snow and wind, would have negative impact on Pinus contorta due to its instability connected to fast-growing trunk and wide crown. Those predictions were hard to estimate since these weather conditions also is common in Canada, where it has its origins. The overall damage caused by snow and wind generates an annual loss of approximately hundred million Swedish crowns (SEK), which has a great impact on the Swedish forestry economy. The awareness of the risks and damages on Pinus contorta makes it possible to both prevent and avoid these to some extent. A more detailed study has been done in the Swedish region Norrbotten, where the level of the damage on Pinus contorta was compared to the Swedish pine. The study included inventory of un-thinned stands and quantified data was collected. The conclusions indicated that the level of damage on Pinus contorta stands was more than twice the damage of the Pinus sylvestris stands. However, since Pinus contorta is relatively new in Sweden, the results regarding the future of the contorta pine are therefore insufficient. Furthermore, it is not possible to predict if the promised increase of production will ever be achieved. To achieved reliable results, further studies are suggested where both loss of profit, compared to the Pinus sylvestris stands, but also the full turnaround time of Pinus contorta are included.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".