Essais de jardinage sans martelage dans des peuplements irréguliers de sapin baumier et d’épinette noire
Bibliographic record
Abstract
It is increasingly accepted that silviculture must now try to maintain complex stands. In this context, selection cutting has been suggested as an option for irregular boreal stands. However, selection cutting in virgin stands could prove difficult to apply at a reasonable cost. In an attempt to reduce harvesting costs, two selection cutting patterns were implemented, avoiding tree marking. The efficacy of this approach was evaluated by comparing the results of plots harvested without tree marking to those with tree marking. To proceed in the absence of tree marking, silvicultural types were defined as well as a rule for the selection of stems to harvest; the final choice of stems to fell was therefore left to the operator upon harvesting. The effects of the different silvicultural and tree marking treatments were examined in relation to their ability to maintain the main structural and functional attributes of irregular boreal stands. A follow-up was conducted with regards to structure, composition, residual stand basal area, abundance of woody debris and mortality. The absence of marking did not affect the performance of selection cutting treatments in terms of basal area, quadratic mean diameter and Shannon index. Tree vigour was not modified by harvesting, both with and without tree marking. Stand composition was not influenced by the absence of marking. Snag abundance was reduced to similar levels regardless of marking. Tree marking did not influence the abundance of downed woody debris. As a consequence, it seems possible to apply a simplified approach of selection cutting, without compromising the success of the treatment in these stands.Key words: black spruce, Picea mariana, balsam fir, Abies balsamea, irregular stand, selection cutting, ecosystem management, forest attributes
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".