A watershed fragility index for assessing the vulnerability of river ecosystems
Bibliographic record
Abstract
Accelerating impacts of climate change have heightened the vulnerability of ecosystems, posing critical challenges to biodiversity conservation. While current climate change vulnerability assessment frameworks provide valuable insights, they often fall short of fully integrating local stressors. This paper introduces the Watershed Fragility Index (WFI), an innovative tool designed to address these gaps by offering a more comprehensive evaluation of multiple stressors. The WFI leverages Geographic Information Systems (GIS) for spatial analysis, Fuzzy logic for handling ecological complexity and variation, and the Analytic Hierarchy Process (AHP) for prioritizing stressors. For a better comprehension of various exposures, 12 factors are assessed − flooding susceptibility, temperature change, wildfire potential, soil type, geology, distance from waterbodies, slope, altitude, land use and cover, distance from roads, watercourse barriers, and forest change. They are organized into different sub-indexes related to natural disturbances, environmental fragility, and anthropogenic stressors. The tool is demonstrated on the Humber River watershed in the province of Newfoundland and Labrador, in eastern Canada. The results indicate that certain stressors create vulnerable areas near important lakes. Overall, the watershed is classified predominantly as low fragility. However, the most vulnerable regions, characterized by moderate fragility, are mainly found in the Lower Humber area, which also contains a larger area with roads, watercourse barriers, and steeper slopes. The outputs offer crucial insights to aid environmental planning within the Humber River watershed and can serve as an evaluation tool for other regions. The WFI is a novel tool for policy development, enabling environmental managers and conservationists to create targeted and adaptive strategies that enhance habitat and species resilience through comprehensive and integrated assessment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".