Mise au point d'une sylviculture adaptée à la forêt boréale irrégulière
Bibliographic record
Abstract
The Canadian boreal forest covers a wide territory within which the natural disturbance regime varies widely. The specific dynamics of the eastern portion is responsible for an abundance of stands of irregular structure, which influences ecosystem biodiversity. Partial cuts should therefore play an important role in an adapted silviculture that focuses on maintaining biodiversity. However, the practice of partial cuts in the context of irregular boreal forests still needs to be developed. In this context, an integrated experiment comparing the current harvesting practices (careful logging preserving advance regeneration, cutting leaving small merchantable stems) and two selection cutting methods was put in place. It will enable us to compare the effect of these practices on operational plans, silviculture, wildlife and wood processing. This experiment has already shown that it is possible to operationally maintain a well-developed stand structure after cutting. Both selection cutting approaches have led to increases in harvesting costs but these were kept low. Future monitoring will clarify the effects of these treatments in terms of vegetation and wildlife, and whether gains can be obtained when processing wood from partial cuts. This project is part of the research program of the Industrial Research Chair NSERC-Laval University in silviculture and wildlife. Key words: irregular stands, selection cutting, biodiversity
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".