Assessing Environmental Impact Within Canada’s Gwich’in Settlement Region
Bibliographic record
Abstract
Environmental changes are impacting northern environments and human communities. Cumulative impact assessments are vital to understanding the combined effects of regional industrial developments and natural disturbances that affect humans and ecosystems. A gap in cumulative impacts literature includes methods to evaluate impacts in cultural landscapes. In this study, we utilized spatial overlay analysis to assess cumulative environmental impacts in the cultural landscape of northern Canada’s Gwich’in Settlement Region. In three analyses, we quantified and mapped: (1) Cultural feature density, (2) cumulative environmental disturbance, and (3) potential overlap between disturbances and cultural features. Our first analysis depicts the extent and pattern of cultural relationships with regional landscapes and illustrates the Gwich’in cultural landscape, with widespread harvesting trails, named places, traditional use areas, and archaeological sites found in highest densities near important waterways. Our second analysis suggests that spatial overlay can track multiple disturbances, illustrating diffuse, lower intensity cumulative environmental impacts. The final analysis shows that overlaying disturbance and cultural feature data provides a novel way to investigate cumulative impacts in a cultural landscape, indicating relatively low levels of potential overlap between Gwich’in cultural features and disturbances. These methods provide one way to investigate cumulative impacts, relevant for well- documented cultural landscapes.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".