Chemainus River watershed: Water quality report for the 2023/24 wet season
Bibliographic record
Abstract
Water is essential for life, and steps are needed to understand, protect and restore its health in fish habitat throughout British Columbia. The Raincoast Healthy Waters program was launched in 2023 to establish community-oriented water pollution monitoring in select BC watersheds. Two Healthy Waters sampling events take place every year in each watershed, the first in the dry season (summer), and the second being in the wet season (winter). This report highlights results from the first wet (winter) season sampling carried out with the support and participation of Halalt First Nation. Briefly, the Healthy Waters – Halalt team determined basic water properties (temperature, conductivity, pH, dissolved oxygen and turbidity) in situ at sampling sites on December 11, 2023. Water samples were collected from five water categories, including source water (3 sites), stream and river water (3 sites), road runoff (3 sites), and tap water (10 samples). Marine water samples (3 locations) were collected on February 6, 2024. Samples were pooled by water category and analysed for coliform, metals, nutrients and physical parameters, pesticides, polycyclic aromatic hydrocarbons (PAHs), pharmaceuticals and personal care products (PPCPs), polychlorinated biphenyls (PCBs), alkylphenol ethoxylates, bisphenols, per- and poly-fluoroalkyl substances (PFAS), sucralose and 6PPD-Quinone. Overall, the Chemainus River watershed had relatively good water quality in the wet season, but additional sampling and analysis will provide additional insight into contamination impacts from forest fires, domestic wastewater, industrial chemicals and road runoff on the health of this valued watershed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".