Development of an effects-based approach for watershed scale aquatic cumulative effects assessment
Bibliographic record
Abstract
Environmental impacts can manifest themselves in a cumulative manner over very large spatial (watershed) and temporal (decadal) scales. In response to these challenges, scientists have been developing methods that attempt to assess the complex interactions between our environment and the current and future demands of society. This article proposes a framework for quantifying cumulative changes in water quality and quantity and demonstrates its implementation in an entire watershed, the Athabasca River Basin in Alberta, Canada. The Athabasca River Basin is an ideal watershed for this study as it has undergone significant increase in urban and industrial developments that have the potential to impact this aquatic ecosystem. This framework addresses the problems of setting a historical baseline and comparing it to the current state in a quantitative way. This framework also creates the potential for predicting future impacts by creating thresholds specific to the study area. The outcome of this framework is the identification and quantification of specific stressors (dissolved Na, chloride, and sulfate) showing significant change across the entire Athabasca River Basin, as well as the development of thresholds for these parameters. This information can be used in future assessments of proposed development and possible mitigation in the basin.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".