Real-Time Water Quality Monitoring as a Regulatory Tool for Mining Sites — The Newfoundland and Labrador Experience
Bibliographic record
Abstract
In order to assess the impact of natural resources development projects such as mines on water bodies, it is crucial to have an appropriate water quality-monitoring program in place. Real-time water quality (RTWQ) monitoring is suited to monitoring the impact of mining projects especially those situated in remote locations. To illustrate the successful use of RTWQ monitoring as a regulatory performance tool, case studies are presented of two real time water quality networks that were established as a part of the environmental permitting process for the Voisey's Bay Nickel Mine project site in Labrador and the Aur Resources Copper Zinc Mine site in Newfoundland. The case studies detail the need for the establishment of the networks, the installation of the networks, the details of the networks, the Quality Assurance program, the RTWQ public web page and the experience encountered since the networks were established. Also discussed is the use of the data collected by the RTWQ network by various stakeholders. The challenges of using RTWQ for mining sites are also discussed.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".