Linking stand attributes to cartographic information for ecosystem management purposes in the boreal forest of eastern Québec
Bibliographic record
Abstract
In the North American boreal forest, the adoption of forest ecosystem management strategies usually increases the number of forest stands to be treated with irregular or uneven-aged silvicultural systems. However, it is difficult to properly target the stands most appropriate for partial cut treatments in remote areas where road access is limited, because current inventory data do not include an assessment of key stand characteristics for silvicultural prescriptions, such as the abundance of small stems in the understory. In this study, we present a forest classification performed using classification and ordination methods, based on ground-measured structural and compositional stand characteristics, in a region of eastern Québec, Canada. This classification resulted in six forest types, which range in composition and structure from relatively regular post-fire stands dominated by black spruce (Picea mariana) to relatively irregular stands co-dominated by balsam fir (Abies balsamea) and black spruce. This classification was linked with cartographic information currently available to forest managers. Information from a fine-scale forest map predicted slightly better the presence of forest types with irregular stand structures compared with a coarse-scale forest map complemented with a fire map. Thus, areas most suitable for the implementation of uneven-aged silvicultural systems can be roughly delineated from existing cartographic information, which will facilitate their integration into large-scale and long-term forest management plans.Key words: boreal forest, forest classification, forest dynamics, stand structure, silviculture
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".