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Record W4401713666 · doi:10.1080/15230430.2024.2376992

Do gravel highways affect water quality and invertebrate communities in Arctic lakes?

2024· article· en· W4401713666 on OpenAlexaffabout
Madeline E. Patenall, Natasha M. Hannan, Vivian Gao, Homa Kheyrollah Pour, Derek K. Gray

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsInvertebrateEnvironmental scienceWater qualityArcticTundraEcologyBorealHabitatHydrology (agriculture)GeologyBiology

Abstract

fetched live from OpenAlex

Gravel roads are a common feature in developed areas of Canada’s Arctic. These roads can be a source of calcareous dust that drifts to roadside lakes, causing significant changes in conductivity, calcium, and pH levels. In this study, we examined if road proximity was associated with differences in water quality and invertebrate communities in lakes along the Dempster and Inuvik-Tuktoyaktuk Highways in the Northwest Territories, Canada. We collected biological and water quality data from 18 lakes selected using a stratified random sampling design, with distance from the road (0–300 m, 300–600 m, and >600 m) and region of study (boreal forest, tundra) as the two factors. We hypothesized that lakes closer to the road would exhibit differences in water quality and invertebrate communities associated with road dust pollution and other stressors caused by roads. We found no clear differences in water quality or invertebrate communities among lakes based on distance from the highways. In addition, while there were differences between regions, these did not appear to be related to the effects of the roadways. Our results suggest that variability in lake morphometry and water quality in this region might be more important than the influence of roads.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.341
Teacher spread0.269 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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