Low extent but high impact of human land use on wetland flora across the boreal oil sands region
Bibliographic record
Abstract
Boreal wetlands are at risk of degradation from anthropogenic activities including oil sands energy extraction. Despite efforts to monitor the impacts of oil sands energy extraction-related activities on wetland ecology, few studies examine the impacts of diverse human development types on wetland plant communities. Here, we sought to quantify the effects of human development in the Athabasca, Peace River, and Cold Lake Oil Sands Regions in Alberta, Canada, and to examine its impact on wetland plant community composition. Across the region, we found that total development and development related to energy and mining were both low; ~80% of the study area was undeveloped. Despite the low spatial extent, total anthropogenic development was negatively correlated with site-level conservatism (a metric of plant tolerance to environmental perturbation) in all five wetland classes examined. This suggests that wetlands surrounded by human development are inhabited by generalist species that are tolerant of environmental disturbance. Moreover, distinct floristic groups within each wetland class could be distinguished based on their total developed area, providing additional evidence that human development affects plant composition and diversity, despite its limited extent in the study area. In contrast to total development, energy and mining development had an inconsistent or no detectable impact on wetland plant community composition at the regional level, likely because although oils sands surface mining is intensive, it is spatially restricted to a small area within the oil sands region. Our findings show that wetland plant communities in the oil sands region are impacted by multiple types of human land use concurrently; further research should aim to evaluate both the distinct impacts of different land use types using gradients of development intensity, as well as the cumulative impacts of multiple land use types happening concurrently.
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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 | 0.000 |
| 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".