Assessing Morphological Change in Canadian Boreal Forests
Bibliographic record
Abstract
Boreal forest cover change occurs in Canada primarily due to fire, a process that is predicted to experience a regime modification due to a changing climate. While fire frequency and area burned are relatively easily measured and tracked, we seek to understand whether the morphological structure of fires has also been changing through time or whether differences are detectable among Canadian Provinces and Territories due to jurisdictional or geographic differences. We use jurisdictions as proxies for differing forest management policies and geographic position. This study compares morphological segmentation patterns of annual boreal forest cover change from 2001 to 2014 across the entire Canadian boreal biome. We implement a bootstrapping of join-count results that were computed for each morphological element type and use the means and variability within ANOVA and Levene’s tests for assessing statistically significant differences among our groups (years and jurisdictions). Overall, the morphology of forest disturbance patterns within the Canadian boreal biome was not found to be trending in any specific way, though there were isolated differences detected. We highlight those specific combinations that are particularly interesting within the context of the research questions posed. Our approach is conservative, as to not produce an alarmist response; since we focus on means, and disturbances are likely to emphasize extremes, thus only substantial regime modifications will produce statistically significant results. Interestingly, even with projected increases to fire intensity and area burned, the morphological structure of fire remains relatively stable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".