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Record W7111122436 · doi:10.1021/acs.est.5c00411.s001

FreshwaterSalinization of Seasonal Ponds: High Salinityand Stratification Threaten Critical, Overlooked Habitats

2025· article· W7111122436 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Language
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityHabitatStratification (seeds)Aquatic ecosystemWater columnHydrology (agriculture)SeasonalityPollution

Abstract

fetched live from OpenAlex

Nearly a century of road salt use in the snowbelt region of North America has led to substantial increases in salinity levels in freshwater habitats. Salt pollution in lakes and rivers is well characterized. Lacking are broad insights for seasonal ponds. As critical habitats for many endemic species, these small and often poorly flushed surface waters are especially vulnerable to accumulating high levels of salts and other pollutants. Here, we measured salinity in 165 seasonal ponds, characterizing salt pollution patterns across space, through time, and over depth within ponds. We found that 70% of ponds within 37 m of a road contained salinity levels exceeding Canadian federal guidelines. 54% of ponds within 25 m exceeded less conservative US federal guidelines. Within ponds, the water column was stratified due to the combined density effects of salt and temperature. Bottom waters of polluted ponds were about 57% saltier than near-surface waters, though many were much saltier than this. Compared to lakes and rivers, far more seasonal ponds appear to be compromised by deicing salt, and overall, the concentration of salt appears to be substantially higher. Among aquatic habitats, seasonal ponds are experiencing the most severe impacts of freshwater salinization, with consequent impacts on sensitive aquatic organisms.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.3350.002

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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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