L'effet du patron de répartition des coupes sur les pertes par chablis : étude de cas dans la sapinière à bouleau blanc de l'Est
Bibliographic record
Abstract
The use of clearcutting involves the creation of exposed edges that become vulnerable to windthrow. The spatial distribution of cutblocks can influence the level of damage. This study compares windthrow losses at edges of clearcuts for two spatial distributions of clearcuts in the balsam fir-white birch ecological domain. The level of windthrow was estimated from aerial photographs of five year-old clearcuts. The proportion of windthrow in the first 30 m of edges around clearcuts amounted to 20.4% for grouped clearcuts in comparison with 15.8% for dispersed clearcuts. Dispersed cutting did not modify the level of damage per unit length of edge. However, the greater perimeter for a similar cut area in the dispersed pattern led to a greater amount of damage per area harvested (15.99 vs. 6.34 m3·ha-1). Key words: windthrow, logging edges, landscape patterns, grouped clearcut, dispersed patch cutting, balsam fir
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".