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Record W4404292469 · doi:10.70766/9365.56

Green/Cheakamus watershed: Water quality report for the 2023/2024 wet season

2024· report· en· W4404292469 on OpenAlexfundno aff
Peter S. Ross, Samantha Scott, Marie Noël

Bibliographic record

Venuenot available
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsWatershedEnvironmental scienceWater qualityHydrology (agriculture)GeologyEcologyComputer scienceBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

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). While the Healthy Waters program typically focuses its work within singular watersheds, this partnership featured two Whistler area watersheds: the Green River, which drains through the Lillooet and Fraser Rivers into the Strait of Georgia (watershed area of 875 km2). and the Cheakamus River which drains south via the Squamish River to Howe Sound (watershed area of 1,034 km2). Combined, these watersheds cover an area of 1,909 km2. This report highlights results from the first wet (winter) season sampling carried out with the support and participation of the Whistler Lakes Conservation Foundation (WLCF). Briefly, the Healthy Waters – WLCF team determined basic water properties (temperature, conductivity, pH, dissolved oxygen and turbidity) in situ at sampling sites on November 23, 2023. Water samples were collected from five water categories, including source water (2 samples), stream and river water (7 samples), road runoff (6 samples), tap water (10 samples – pooled into a single composite sample) and marine water (one sample). Samples were then analysed individually for coliform, metals, nutrients and physical parameters, and pooled by water category and analysed for 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 6-PPD Quinone. Several contaminant classes were found at higher concentrations in the dry season, but some were higher in the wet season. Overall, the Green/Cheakamus watersheds 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.564
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.130
GPT teacher head0.336
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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