A simple Bayesian Belief Network for estimating the proportion of old-forest stands in the Clay Belt of Ontario using the provincial forest inventory
Bibliographic record
Abstract
The differences between boreal forest landscapes produced by natural disturbance regimes and landscapes produced by harvesting are important and increasingly well documented. To continue harvesting operations while maintaining biodiversity and other ecosystem services, government policies and certification processes are pushing for practices that preserve landscape features within their range of natural variability. One major shortcoming in the implementation of such a strategy is the lack of complete spatial or temporal information about these landscape features, such as the proportion of old stands, which are believed to act as a coarse filter for conservation if they remain representative enough of natural conditions. The objective of this study was to quantify the proportion of old stands in a very large landscape by combining fragmentary knowledge from two different sources, i.e., a provincial forest inventory and existing fire history reconstructions using a Bayesian Belief Network. This study was conducted over a 6.5 Mha landscape located within the Clay Belt of the province of Ontario, Canada, and suggests that more than 72.4% of this area is occupied by stands where no fire occurred during the last 150 years. The implications for management and potential for future research are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".