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Record W4408320270 · doi:10.70766/76.9001

Tod Creek watershed: Water quality report for the 2023/24 wet season

2024· report· en· W4408320270 on OpenAlexfundno aff
Peter S. Ross, Samantha Scott, Marie Noël

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsWatershedEnvironmental scienceHydrology (agriculture)Water qualityGeologyEcologyBiologyComputer science

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 dry season (summer), and the wet season (winter). This report highlights results from one sampling event: the first wet (winter) season sampling, carried out with the support and participation of the Capital Regional District (CRD) and Tsartlip First Nation. Briefly, the Healthy Waters team sampled the Tod Creek watershed on December 13, 2023. The team worked with CRD, Tsartlip First Nation and community volunteers to first determine basic water properties (temperature, conductivity, pH, dissolved oxygen and turbidity) in situ. Water samples were collected from six water categories, including source water (3 samples), stream and river water (3 samples), road runoff (3 samples), tap water (10 samples - 9 from the Sooke supply and 1 from groundwater were pooled into a single composite sample) and marine water (3 samples), alongside surface water samples collected in the areas surrounding the Hartland landfill (3 samples). Samples were then pooled into a single composite sample for each of the six water categories and analysed for coliform, metals, nutrients, 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 6-PPD Quinone. This initial sampling with a limited number of samples suggests that, overall, Tod Creek water quality was relatively good. Additional sampling and analysis planned will provide additional insight into any sources or activities that may be impacting 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.006

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 head0.046
GPT teacher head0.309
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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