Deriving water quality index using site-specific water quality parameter guidelines for real-time water quality stations
Bibliographic record
Abstract
ABSTRACT The Newfoundland and Labrador Real-Time Water Quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near-real-time. The network generates vast amounts of data which are too complex for many stakeholders to interpret. While the CCME Water Quality Index (WQI) has been a valuable tool to simplify and summarize ambient quality of water, its use in RTWQ is virtually non-existent. This is largely due to the lack of alignment between the limited number of parameters from real-time networks, and availability of aquatic Water Quality Guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site specific water quality guidelines using Background Concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine how score is influenced by stage and precipitation. The influence of each parameter on the RTWQ WQI score is also plotted. Comparison is made between the Canadian Environmental Sustainability Indicator (CESI) WQI and RTWQ WQI yearly scores. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".