Wascana Lake water quality monitoring program
Bibliographic record
Abstract
The 2003-04 Winter rehabilitation of Regina's Wascana Lake (the 'Big Dig') was intended to improve water quality in the lake. It presented a unique opportunity to monitor the quality of lake water from 'day one' by tracking changes in various physical, chemical and biological parameters over the course of the lake's maturity. An existing assessment tool, the CCME Water Quality Index (WQI) was used to evaluate data from water samples taken at several sites within the lake during the first two years. The WQI includes measures of how many parameters exceed national water quality guidelines, how often, and by how much. Data for the first two years show that the lake remains a poor site for recreational activities involving extensive contact with the water (WQI = 32) due to high levels of bacteria (Total coliforms, E. coli, fecal Streptococci) and pH. A significant waterfowl population resides in and around the lake, contributing to the high counts of fecal bacteria. Bacterial counts increased as the lake warmed throughout summer. Wascana Lake provides a marginal environment for wildlife based on the parameters examined (WQI = 57). Phosphorus and nitrogen levels are elevated to levels that may contribute to overgrowth of vegetation and algae.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".