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Record W4388724414 · doi:10.1002/lol2.10367

Salinization, warming, and loss of water clarity inhibit vertical mixing of small urban ponds

2023· article· en· W4388724414 on OpenAlexafffundabout
Charlie J. G. Loewen, Donald A. Jackson

Bibliographic record

VenueLimnology and Oceanography Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaIowa State University
KeywordsEnvironmental scienceEutrophicationUrbanizationSalinityStratification (seeds)Hydrology (agriculture)CLARITYEcosystemGlobal warmingWater columnClimate changeEcologyOceanographyGeologyNutrient

Abstract

fetched live from OpenAlex

Abstract Urbanization drives multiple environmental changes that influence critical ecosystem processes. Factors such as salinization by deicing road salts, reduced water clarity (and greater light attenuation) from eutrophication and sediment loading, and warming constrain not only the biodiversity of ponds, but also their physical mixing (with consequences for oxygen availability and the provision of ecosystem services). Leveraging an extensive urban gradient in the Greater Toronto Area, we collected summertime depth profiles from 50 stormwater retention ponds to investigate their vertical stratification. We found that water columns were generally stratified but contrary to expectations, we found relatively minor roles of basin area and depth. Instead, we discovered an overwhelming effect of salinity along with significant impacts of temperature and water clarity on water density gradients. Findings extend our fundamental understanding of mixing regimes in small, shallow waterbodies and indicate increasing risks to pond functioning in a warmer and saltier future.

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 score0.014
Threshold uncertainty score0.339

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 head0.010
GPT teacher head0.196
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

Citations10
Published2023
Admission routes3
Has abstractyes

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