Dynamique naturelle et aménagement durable de la forêt boréale : apports des modèles basés sur les Transformations de Graphes pour caractériser les trajectoires et fournir des recommandations
Bibliographic record
Abstract
The boreal forest represents a third of the planet's forest cover, i.e. 400 million hectares in North America and more than double that in Eurasia (Johnson 1992, Shugart et al. 1992). It is one of the largest terrestrial reservoirs of carbon (sequestered mainly in soils and biomass) and therefore plays a part in balancing the planet's climate (Dixon et al. 1994). Since the end of the last ice age, the two disturbances that have governed its natural dynamics have been fires and insect invasions (Johnson 1992, Shugart et al. 1992, Jardon et al. 2003, Hély et al. 2010, Blarquez et al. 2015), but forest cutting has also played a significant role over the last century or so (Gauthier et al. 2015b). In Canada, fires are very intense (crown fires) and destroy standing stands (Johnson 1992), but species have developed very effective regeneration strategies (Gauthier et al. 1996, Ali et al. 2008). Over the last few thousand years, climate change has led to significant changes in fire regimes (frequency and surface area), yet the few species that make up the boreal forest have been maintained (Remy et al. 2017b). In fact, it is on this basis of the impact of natural disturbances that sustainable forest management in Canada has been based for about two decades (Bergeron et al. 1999, Harvey et al. 2002, Gauthier et al.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".