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Record W2033176979 · doi:10.1680/wama.11.00017

Estimates of the Manning's coefficient for ice-covered rivers

2012· article· en· W2033176979 on OpenAlexafffundabout
S. Samuel Li

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Water Management · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIce formationHydraulicsHydrology (agriculture)SnowEnvironmental scienceLogarithmGeologyGeotechnical engineeringAtmospheric sciencesGeomorphologyMathematicsEngineering

Abstract

fetched live from OpenAlex

The management of ice-covered rivers presents special issues with respect to planning, design and operation of a water resources project. To address these issues properly entails a good understanding of the hydraulics of ice-covered rivers, where the composite Manning's coefficient is a fundamental parameter. The aim of this paper is to obtain field estimates of the composite coefficient associated with the ice cover and riverbed. Using four different methods, this paper analyses a large volume of winter measurements of water velocities made from shallow ice-covered rivers in Canada. The results show that the composite Manning's coefficient ranges from 0·013 to 0·040 as winter averages, but can vary up to seven-fold through the winter. The slope of the energy grade line is difficult to measure in the field and thus is rarely available; it appears to be about 30% of the water slope. The method for determining the coefficient based on the logarithmic velocity distribution fitted to measurements has shown advantages, as revealed by comparing the results of the four methods. The estimates of the composite Manning's coefficient reported in this paper are useful for modelling ice-covered river flow and determining winter discharges. This is particularly true when site-specific data are unavailable.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.233

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations39
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Water ManagementSame topicArctic and Antarctic ice dynamicsFrench-language works237,207