Bibliographic record
Abstract
The boreal forest circles the high northern latitudes but it is far from a continuous carpet of evergreen trees. Rather, the boreal forest is a patchwork of land cover types in constant flux as they recover from wildfire and then are burned again. This fast turnover of land cover makes the boreal forest particularly susceptible to rapid change in response to climate. Furthermore, the boreal forest is an important component of the climate system that pumps heat into the atmosphere and significantly raises northern hemisphere temperatures year-round. As both a major component of the climate system and a sensitive indicator of climate change, the boreal forest is in a feedback loop. The direction of that feedback loop, positive or negative, depends largely on the strength of the land-atmosphere exchange of heat and momentum driven by forest cover and its spatial structure. That spatial structure has yet to be comprehensively measured. \tThis dissertation used newly available, high resolution, satellite based forest cover data to quantify the heterogeneity of the boreal forest in North America. First, at the local scale, the pattern of forest cover patches within fires were found to be larger, more regularly shaped, and clustered than in unburned forest. The heterogeneity metrics also returned to pre-fire levels relatively quickly. At the continental scale, the landscape heterogeneity maps were analyzed by region, with respect to the northern extent of trees, and disturbance regimes. The boreal forest regions had smaller, more complicated forest patches, and no single dominant forest cover class which was significantly different than the temperate forests that border the region to the south. When compared to two preexisting maps of the boreal treeline, the patch cohesion metric indicated that the tundra ecoregion extended further south into the forested Central and Eastern Canada. Based on this finding, a new patch cohesion-based treeline was drawn which divides the boreal forest and tundra in a standard and repeatable way. Lastly, fires and lakes had the opposite influence on the heterogeneity metric contagion. Fires tended to decrease heterogeneity in the landscape because they were larger than the preexisting forest patches while lakes were smaller and broke up the landscape increasing heterogeneity. The heterogeneity maps produced as a part of this dissertation will continue to provide insight into the spatial pattern of the boreal forest in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".