Development of a water quality index for watercourses downstream of harvested peatlands
Bibliographic record
Abstract
Abstract This study aimed to adapt the Water Quality Index of the Canadian Council of Ministers of the Environment (CCME WQI) for its application to water quality assessment of drainage water and watercourses downstream of peat harvesting operations. It integrates different parameters that potentially reflect the overall water quality condition of a stream. Thus, it is calculated using multivariate water quality data and accounts for their conformity with respect to water quality guidelines. Adaptation of the index proceeded to identify, through a literature review, the physico-chemical parameters that may change due to peat harvesting. The CCME WQI was used to compare water quality of receiving watercourses to that of streams located within a 200 km radius from the study sites in three regions of Quebec. The availability of water quality data guided the selection of parameters among those identified. They are ammonia, conductivity, pH and suspended sediment concentrations. Results indicated a significant difference between WQI values of water from harvested peatlands and those of streams in two of the three regions studied. Results have also shown that it is the pH guideline that is not respected in most cases for harvested peatlands. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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 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".