Water Quality Monitoring to Support Cumulative Effects Assessment and Decision Making in the Mackenzie Valley, Northwest Territories, Canada
Bibliographic record
Abstract
Project proponent- and government-led environmental monitoring are required to identify, understand, and manage cumulative effects (CE), yet such monitoring initiatives are rarely mutually supportive. Notwithstanding the need for a more integrated and complementary approach to monitoring, monitoring efforts are often less effective than intended for addressing CE. This paper examines current monitoring programs in the Mackenzie Valley, Northwest Territories, Canada, based on 7 attributes: consistency, compatibility, observability, detectability, adaptability, accessibility, and usability. Results indicate a tenuous link between and across proponent-led monitoring requirements under project-specific water licenses and government-led monitoring of regional baseline conditions. There is some consistency in what is monitored, but data are often incompatible, insufficient to understand baseline change, not transferable across projects or scales, inaccessible to end users, and ultimately unsuitable to understanding CE. Lessons from the Mackenzie Valley highlight the need for improved alignment of monitoring efforts across programs and scales, characterized by a set of common parameters that are most useful for early detection of cumulative change and supporting regulatory decisions at the project scale. This alignment must be accompanied by more open and accessible data for both proponents and regulators, while protecting the sensitivity of proprietary information. Importantly, there must be conceptual guidance for CE, such that the role of monitoring is clear, providing the types of CE questions to be asked, identifying the hypotheses to be tested, and ensuring timely and meaningful results to support regulatory decisions. © 2019 SETAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".