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Record W4223473080 · doi:10.1002/etc.5341

Delineating Effluent Exposure and Cumulative Ecotoxicological Risk of Metals Downstream of a Saskatchewan Uranium Mill Using Autonomous Sensors

2022· article· en· W4223473080 on OpenAlexafffundabout
Beatriz Cupe‐Flores, Maíra Peixoto Mendes, Banamali Panigrahi, Karsten Liber

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of Saskatchewan
FundersGlobal Water Futures
KeywordsEnvironmental scienceEffluentMercury (programming language)Aquatic ecosystemSedimentCadmiumEnvironmental chemistryWater qualityEcotoxicologyHazard quotientRisk assessmentWater pollutionHydrology (agriculture)EcologyEnvironmental engineeringChemistryHeavy metalsGeologyBiology

Abstract

fetched live from OpenAlex

There is increasing interest in using autonomous sensor technology to monitor aquatic ecosystems in real time and in employing such monitoring data to perform better ecological risk assessments. At seven locations in McClean Lake in northern Saskatchewan (Canada) that received diluted uranium milling effluent, we deployed sensor units to track effluent distribution and help predict potential biological effects on aquatic invertebrates. Water was also collected from each location on multiple occasions to measure major ions, dissolved metals, and routine water quality, and sediment was sampled to analyze total metals. The ecotoxicological risk to aquatic invertebrates was estimated using hazard quotients (HQs). The cumulative risk was estimated by summing the individual HQs, and the major ions risk was based on total osmolarity. The results indicated temporal and spatial variations in effluent exposure based on sensor electrical conductivity (EC) measurements in the McClean Lake East Basin. Individual HQs for water ranged from "moderate" (0.40-0.69) to "very high" (greater than 1) for silver, cadmium, arsenic, selenium, mercury, iron, and thallium. At all sites, major ions risk was less than 1. Individual HQs for sediment were "moderate" (0.40-0.69), "high" (0.7-0.99), and "very high" (greater than 1) for vanadium and cadmium. The cumulative risk in water and sediment for all metals combined was greater than 1 at some sites in Vulture Lake (which discharged into McClean Lake) and in McClean Lake itself. A more detailed estimation of the risks for aqueous selenium and arsenic (the only two metals that had good correlation with sensor EC data) indicated that their 90th percentile HQ values were less than 1 in McClean Lake, suggesting that these contaminants of concern do not represent a significant direct risk to aquatic invertebrate communities. Environ Toxicol Chem 2022;41:1765-1777. © 2022 SETAC.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.214
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2022
Admission routes3
Has abstractyes

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