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Hydraulic modelling for assessment of the performance of sedimentation basins downstream from extracted peatlands

2020· article· en· W3101604364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMires and Peat · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPeatSedimentationHydrology (agriculture)Downstream (manufacturing)Environmental scienceGeologyGeomorphologyGeographyGeotechnical engineeringEngineeringSedimentArchaeology

Abstract

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Three sedimentation basins on two different extracted peatlands were studied to determine their Trapping Efficiency (TE) using two different methods. First, TE was calculated using sediment loads estimated from turbidity measurements upstream and downstream of the basins. The second method was based on hydraulic modelling and a simplified sediment deposition model. For the first studied basin (controlled by a weir at its downstream end) TE was estimated with the second method at 85.9 % and 55.6 % for lower and higher flows, respectively. In the second peatland the studied basins were in series, there was a geotextile curtain in the middle of each basin and a weir or a double pipe culvert at the outlet. For these two basins in series, TE was estimated at 80 % for lower flows and at 34.3 % for higher flows. A hydraulic model was calibrated for the studied basins and applied to estimate the TE of different basin configurations. The results show that the role of the geotextile curtain is important in the case of short basins and for intense rainfall events. The double pipe culvert did not have a significant effect on TE, unlike the presence of a weir at the outlet, which is required to maintain high TE.

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.

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 score0.362
Threshold uncertainty score0.184

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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