Tracking water quality in a changing climate
Bibliographic record
Abstract
Both the quality and quantity of water in watersheds is expected to be altered due to climate change. However, there is insufficient scientific knowledge to predict how these changes are likely to be manifested. One approach is to look at long-term trends in past data to evaluate changes over time and space (e.g. watershed and regional variability). CanmetMINING, in collaboration with Environment and Climate Change Canada are compiling national and provincial water quality data in areas located close to active and abandoned mine sites. By compiling and examining water quality data over the past couple of decades we aim to determine if baseline water quality is changing over time, which could reflect climate change impacts. This work will help us to identify a number of important observations: 1) Identify indicators – are there specific metals or water quality parameters that show consistent responses to climate change that could be used as ‘indicators’ for future projections. 2) Hot spots – are there specific areas in Canada that are more sensitive to changes in climate? By collaborating with provinces and the federal family this project provides an opportunity to establish a national database that could track and monitor water quality in a changing climate, which would be of use to research organizations, governments as well as offering potential for citizen science applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".