Bibliographic record
Abstract
A new method for measuring the silvicultural result of thinning is presented in the study. The measuring method was based on rectangular sample plots measured parallel to strip roads. An individual sample plot consisted of eight zones, each 30 m2 in area. Due to its considerable importance in Finland, the one-grip harvester operation was the harvesting system examined. The research material was collected from 15 stands amounting to a total area of 14.7 ha. The post-harvesting inventory provided good information on the removed and standing trees, their size and distribution. The number and distribution of standing and removed trees were according to Finnish thinning instructions, and thinning was typical low thinning, in which smaller trees and trees of low quality are removed. The average tree damage percentage, 4.6, is acceptable. However, the proportion of damage varied from 1.1% to 9.1% with different operators. The damage was highest during the summer. Small, superficial damage was typical. The average strip road width was 4.8 m, the distance between strip roads 19.8 m and the rut depth 0.6 cm. The economic consequences of the damage was estimated using a calculation model. The model estimates the losses caused by strip roads, tree and soil damage. The economic consequences of harvesting damage during the rotation period was 1158 FIM (1 U$ = 5.60 FIM). Strip roads make a significant contribution to the amount of costs. Due to the high variation in the harvesting quality, both the continuing supervision of the silvicultural thinning result and the training of machine operators are necessary. Thinning spruce stands during the sap period should be avoided due to the high risk of tree damage, and decay following damage. Generally, it is possible to obtain a good silvicultural thinning result with one-grip harvester operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".