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Record W4281638898 · doi:10.1007/s10661-022-10117-5

Water temperature variability at culvert replacement sites and river thermal impacts related to the removal of an old sediment pond: application on the Barnet Brook and a tributary of the Nerepis River (New Brunswick, Canada)

2022· article· en· W4281638898 on OpenAlexaffabout
Daniel Caissie, Andy Smith

Bibliographic record

VenueEnvironmental Monitoring and Assessment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsCulvertTributaryHydrology (agriculture)STREAMSEnvironmental scienceVegetation (pathology)SedimentGeologyGeotechnical engineeringGeomorphologyGeography

Abstract

fetched live from OpenAlex

Culverts are very important hydraulic structures for stream crossing, and they come in various shapes and materials. There are generally two different types of culverts, i.e., closed bottom and open bottom structures. In the present study, two closed bottom culverts have been replaced by open bottom structures (arch culverts) during the summer of 2018. The objective of the present study was to analyze water temperature variability along the impacted sites, one year after the replacement, i.e., 2019 to assess potential impacts of the streamside vegetation removal on the thermal conditions of these streams. Results showed a significant (p < 0.05) change in mean summer temperatures at both sites. Changes in stream temperatures at Barnet Brook were attributed to the removal of an old sediment pond, whereas changes in stream temperatures at the tributary of the Nerepis River were likely due to the removal of the streamside vegetation. Increases in water temperatures (> 4 °C) were more pronounced during low flow periods compared to high flow conditions at both sites.

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)0.001
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 score0.159
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.201
Teacher spread0.197 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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