Influence of edge effects on forest structure adjacent to oil sands disturbances in boreal forests
Bibliographic record
Abstract
Industrial development in Alberta’s oil sands region has created a dense network of anthropogenic forest edges, altering forest structure, composition, and microclimates. This thesis examines how edge effects influence forest structure in mature upland boreal forests associated with adjacent in situ oil sands footprints. Field data were collected from 46 edge sites in the Athabasca oil sands region during the 2023 and 2024 growing seasons. I compared narrow linear disturbances (seismic lines) and larger polygonal clearings (abandoned wellpads) to assess tree mortality, recruitment, stem density (Chapter 2), and the availability of coarse woody debris (CWD) and snags (Chapter 3). Edge effects were more pronounced next to larger gaps, but varied substantially by edge orientation. West-facing edges, exposed to prevailing winds, were associated with higher probabilities of large-tree mortality and snag formation. Tree recruitment declined with distance from the edge but was higher along west-facing edges than south-facing ones. Stem densities of trees peaked within 20 m of edges, illustrating an edge-sealing effect. Recently fallen CWD was sparse near edges, while older CWD and snags were shaped by edge distance and edge exposure. Overall, this study demonstrates that edge orientation, gap size of adjacent footprints, and time since disturbance interact to shape forest dynamics and CWD availability along anthropogenically-derived forest edges within Alberta’s oil sands region. These findings highlight the nuanced impacts of oil sands development on forest structure for increasingly dissected forests and underline the importance of considering edge design into reclamation planning to maintain biodiversity and support species that rely on CWD and snags, such as saproxylic species and cavity-nesting birds like the pileated woodpecker (Dryocopus pileatus).
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".