Portrait préindustriel dans un contexte de grande variabilité naturelle: une étude de cas dans le centre du Québec (Canada)
Bibliographic record
Abstract
Pre-industrial forest cover portrait is a recognized method for establishing the bases of sustainable forest management. However, it is a spatially and temporally dependant concept that should be used with caution in presence of big fires. The objective of the study was to portray the pre-industrial landscape in a mixedwood temperate forest in central Quebec at different spatial scales. The study was based on archival records from a forest company. The pre-industrial forest cover landscape of our study area was mainly composed of mature or old-growth (>100 years) stands and dominated by mixedwood forest stands with intolerant hardwoods. The main tree species were white birch, black spruce and jack pine, three species associated to forest succession after fire in the boreal forest. Considering the great variability caused by the fires and partial knowledge of this variability, for each spatial scale considered, we propose some management targets based on the main pre-industrial characteristics of this forest. To respect the pre-industrial variability, our study suggested that silviculture should be adapted at different landscape scales. Cover types and age class targets should be based on main preindustrial characteristics at each landscape scale analyzed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 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) | 0.004 | 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".