Developing Indicators for Regional Water Quality Assessment: An Example from British Columbia Community Watersheds
Bibliographic record
Abstract
An increased understanding of regional surface water quality and the key factors which differentiate regional from local differences is necessary for monitoring impacts such as mountain pine beetle infestation and related land management practices. This study develops a framework to identify water quality indicators which differentiate parameters influenced by rock type, by relatively short term anthropogenic activities, and those resulting from longer term climatic variability. Rock type was an overriding factor related to stream water chemistry in this British Columbia case study; with differences between watersheds differentiated by Ca, EC, Al and Fe. Grouping watersheds by their dominant rock type permitted the investigation of water quality with other watershed characteristics. The % forest cover, % pine cover, and dominant runoff processes demonstrated significant relationships with soluble cations, metals, turbidity and total organic carbon. Turbidity levels showed low variability, and relationships with mountain pine beetle were not strong; suggesting the need for more detailed data sets, selective monitoring of storm events, and longer term monitoring to improve predictive capacity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".