Fragmentation regimes of Canada's forests
Bibliographic record
Abstract
Canada is a large nation, approximately 1 billion hectares in size, and until recently, no national assessment of forest fragmentation had been undertaken. To assess national level biodiversity and ecosystem condition, national drivers of forest fragmentation are identified as being either primarily natural (e.g., resulting from wildfires, water features, or topography), or primarily anthropogenic (e.g., resulting from urbanization or roads and associated activities such as forest harvesting and oil and gas exploration). The relative importance of each of these fragmentation drivers within Canada's ten forested ecozones, which occupy approximately 650 million ha, is assessed using ecozone summaries and standard scores. Forest pattern metrics were generated from a Landsat‐derived land cover product and fragmentation drivers were characterized using available national datasets. Through this analysis, we combine and portray the relative importance of forest patches with spatial layers indicative of natural and anthropogenically induced conditions as driving various fragmentation regimes over the forested area of Canada. The forest fragmentation in Canada can be characterized primarily by natural drivers, whereas fragmentation regimes attributable to anthropogenic drivers are typically regionally located and related to industrial activities and access (i.e., roads). We identify three scenarios in our results that characterize forest fragmentation in Canada: ecozones with similar forest patterns but different drivers; ecozones with similar patterns and drivers; and finally, ecozones with both different patterns and different drivers. Our findings indicate that national assessments of forest fragmentation should account for both natural (and inherent) and anthropogenic sources of fragmentation .
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".