Windthrow After Shelterwood Cutting in Balsam Fir Stands
Bibliographic record
Abstract
Abstract The use of partial cutting in balsam fir stands has been greatly restricted by the fear of windthrow. This applies to shelterwood cutting, for which very little quantitative information on windthrow is available. This study was conducted in 50-yr-old balsam fir stands. The aim of the study was to quantify windthrow losses associated with three patterns of seed cuts. The study consists of five replicates of four treatments: uncut control, uniform shelterwood, group shelterwood, and strip shelterwood. Complete windthrow monitoring was performed at 2, 4, and 6 yr after cutting. The effect of treatment, wind exposure, and stand characteristics was assessed after 6 yr. A simulation with the ForestGales model was conducted to better understand the seed cut pattern effect in identical stands. Results showed that a shelterwood method involving a low intensity seed cut can be applied in relatively sheltered balsam fir stands. Topographic exposure and stand characteristics did not contribute to the amount of windthrow observed. The major factor explaining the amount of windthrow seems to be the presence of adjacent cuts that funneled wind into the plots. North J. Appl For.20(1):5–13.
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 | 0.000 | 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".