Government of Manitoba Long Term Water Quality Monitoring Program
Bibliographic record
Abstract
The Government of Manitoba’s Long Term Water Quality Monitoring Program supports the Department’s mandate to provide services to protect and maintain the quality of Manitoba’s aquatic ecosystems by developing and implementing coherent long term water quality monitoring programs and activities that are responsive to present and future needs, issues and pressures. Water quality data are collected through grab samples. These samples are sent for laboratory analysis where they can be analyzed for a suite of 120 water quality variables including general chemistry, nutrients, major ions, metals, pesticides and pharmaceuticals/estrogens. The four site locations presented here are only a small subset of the 65 site locations monitored through the program. These four site locations represent four major river systems that flow into Lake Winnipeg. Samples are collected monthly from each of these four sites, with the exception of the Dauphin River, which is collected quarterly. The data are used by the Government of Manitoba in a variety of ways to assess water quality conditions, through technical reports including trends, loads, and the Canadian Council of Ministers of the Environment Water Quality Index, to name a few. The data are also used for water quality modelling and to inform policy decisions associated with Environment Act processes and Integrated Watershed Management Planning, to name a few. These data are also frequently requested and used externally by a number of stakeholders, consultants, academia and non-government organizations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".