A framework for assessing cumulative effects in watersheds: An introduction to Canadian case studies
Why this work is in the frame
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Bibliographic record
Abstract
From 2008 to 2013, a series of studies supported by the Canadian Water Network were conducted in Canadian watersheds in an effort to improve methods to assess cumulative effects. These studies fit under a common framework for watershed cumulative effects assessment (CEA). This article presents an introduction to the Special Series on Watershed CEA in IEAM including the framework and its impetus, a brief introduction to each of the articles in the series, challenges, and a path forward. The framework includes a regional water monitoring program that produces 3 core outputs: an accumulated state assessment, stressor-response relationships, and development of predictive cumulative effects scenario models. The framework considers core values, indicators, thresholds, and use of consistent terminology. It emphasizes that CEA requires 2 components, accumulated state quantification and predictive scenario forecasting. It recognizes both of these components must be supported by a regional, multiscale monitoring program.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it