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Record W4413836341 · doi:10.1002/lol2.70060

Road salt pollution shifts urban stormwater ponds toward cyanobacterial dominance

2025· article· en· W4413836341 on OpenAlexafffundabout
Charlie J. G. Loewen, Donald A. Jackson, Jenna Cook, Rolf D. Vinebrooke

Bibliographic record

VenueLimnology and Oceanography Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of AlbertaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaIowa State University
KeywordsStormwaterDominance (genetics)Salt lakePollutionEnvironmental scienceWater resource managementGeographyEnvironmental engineeringEnvironmental planningHydrology (agriculture)EcologySurface runoffGeologyBiologyGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

Abstract Urban environments contend with an array of stressors, including salinization by deicing road salts. To advance understanding of how road salt pollution affects aquatic ecosystem functioning, we surveyed primary producers in 50 stormwater ponds in Brampton, Canada. We found that chloride concentrations decreased (benthic) periphytic algal biomass but had no detectable effect on the total biomass of (free‐floating) phytoplankton. However, impacts were obscured by underlying compositional shifts, as cyanobacteria generally compensated for declines of other taxa. Varying sensitivities of taxonomic groups (inferred from diagnostic pigments) revealed potential bioindicators, with the proportion of periphytic chromophytes declining most significantly and effects on the relative concentrations of green algae differing between planktonic and benthic communities. As chloride concentrations were a leading predictor of cyanobacterial dominance in our study of impaired, nutrient‐rich, urban ponds, findings reveal an emerging risk of potentially harmful organisms from the ongoing salinization of freshwater resources.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.238
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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.

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

Citations2
Published2025
Admission routes3
Has abstractyes

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