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Record W4292714714 · doi:10.1002/lol2.10278

Interactive effects of increasing chloride concentration and warming on freshwater plankton communities

2022· article· en· W4292714714 on OpenAlexafffundabout
Alexandra McClymont, Shelley E. Arnott, James A. Rusak

Bibliographic record

VenueLimnology and Oceanography Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsMinistry of the Environment, Conservation and ParksQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZooplanktonPlanktonMesocosmEnvironmental sciencePhytoplanktonChlorideAquatic ecosystemSpecies richnessFreshwater ecosystemWater qualityEnvironmental chemistryEcologyEcosystemOceanographyChemistryBiologyNutrientGeology

Abstract

fetched live from OpenAlex

Abstract Across the Northern hemisphere, chloride concentrations in lakes are increasing from decades of road salt use, threatening aquatic ecosystems. Zooplankton are sensitive to increasing chloride but few studies have considered how additional stressors, such as soft water or increasing lake temperatures interact with the negative effects of chloride. To determine the effects of increasing chloride and temperature on freshwater zooplankton and phytoplankton communities, we conducted a mesocosm experiment using 30 concentrations of chloride from ambient levels to 1500 mg L −1 , ambient or warmed (+2.7°C) temperatures, and plankton from a softwater lake. We compared the results with current Canadian Water Quality Guidelines (CWQG) for chronic exposure to chloride. Although warming elicited little response, increasing chloride concentrations led to reduced zooplankton biomass and richness, even at concentrations below the CWQG, and an increase in cyanobacteria abundance, suggesting that policies regulating road salt application should be reevaluated to better protect aquatic ecosystems.

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 score0.079
Threshold uncertainty score0.331

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 studies0.0000.000
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 head0.185
Teacher spread0.181 · 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

Citations22
Published2022
Admission routes3
Has abstractyes

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