Shrub changes with proximity to anthropogenic disturbance in boreal wetlands determined using bi-temporal airborne lidar in the Oil Sands Region, Alberta Canada
Bibliographic record
Abstract
In this study, we used bi-temporal airborne lidar data to compare changes in vegetation height proximal to anthropogenic disturbances in the Oil Sands Region of Alberta, Canada. We hypothesize that relatively low-impact disturbances such as seismic lines will increase the fragmentation of wetlands, resulting in shrub growth. Bi-temporal lidar data collected circa 2008 and 2018 were used to identify correspondence between the density of anthropogenic disturbances, wetland shape complexity and changes in vegetation height within >1800 wetlands near Fort McKay, Alberta, Canada. We found that up to 50% of wetlands were disturbed by anthropogenic disturbance in some parts of the region, with the highest proportional disturbance occurring within fens. Areas of dense anthropogenic disturbance in bogs resulted in increased growth and expansion of shrubs, while we found the opposite to occur in fens and swamps during the 10-year period. Up to 30% of bogs had increased shrubification, while shrub changes in fens and swamps varied depending on density of disturbance and did not necessarily correspond with shrub growth. As wetland shapes became increasingly elongated, the prevalence of shrubs declined between the two time periods, which may be associated with hydrological drivers (e.g. elongated may indicate surface and ground-water discharge influences). The results of this study indicate that linear disturbances such as seismic lines, considered to have relatively minimal impacts on ecosystems, can impact proximal wetland shape, fragmentation and vegetation community changes, especially in bogs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".