Insights into Boreal Forest Disturbance from Canopy Stability Index
Bibliographic record
Abstract
The world’s forests are being increasingly disturbed from exposure to the compounding impacts of land use and climate change, in addition to natural disturbance regimes. Boreal forests have a lower level of deforestation compared to tropical forests, and while they have higher levels of natural disturbances, the accumulated impact of forest management for commodity production coupled with worsening fire weather conditions and other climate-related stressors is resulting in ecosystem degradation and loss of biodiversity. We used satellite-based time-series analysis of two canopy indices—canopy photosynthesis and canopy water stress—to calculate an index that maps the relative stability of forest canopies in the Canadian provinces of Ontario and Quebec. By drawing upon available spatial time-series data on logging, wildfire, and insect infestation impacts, we were able to attribute the causal determinants of areas identified as having unstable forest canopy. The slope of the two indices that comprise the stability index also provided information as to where the forest is recovering from prior disturbances. The stability analyses and associated spatial datasets are available in an interactive web-based mapping app. that can be used to map disturbed forest canopies and the attribution of disturbances to human or natural causes. This information can assist decision-makers in identifying areas that are potentially ecologically degraded and in need of restoration and those stable areas that are a priority for protection.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".