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Record W2157178917 · doi:10.1139/l11-037

New and improved channel cross section with piecewise linear or smooth sides

2011· article· en· W2157178917 on OpenAlexaffvenue
Said M. Easa

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPiecewise linear functionSection (typography)Channel (broadcasting)Cross section (physics)PiecewiseGeometryFlexibility (engineering)Mathematical analysisRange (aeronautics)Topology (electrical circuits)PhysicsMathematicsComputer scienceEngineeringCombinatoricsTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

This paper presents a new and improved channel cross section with m-segment linear sides and horizontal bottom (MSLS). For large m, the section sides become smooth curves, thus providing the designer with flexibility in using either piecewise linear or smooth channel sides with the same formulation. General simple formulas for the area and perimeter are presented for section sides with m linear segments. An optimization model, which implements the general formulas and minimizes the construction cost, is presented and applied using an example. For sections with piecewise linear sides, where the surface lining unit cost increases as the number of sides increases, the MSLS section was found to be more economical than a section with two-segment linear sides when the rate of increase in cost is not large. The smooth MSLS was found to be always more economical than the two-segment parabolic side section and the parabolic side section. The MSLS, which is more economical, yet simpler, than other section types is useful for a wide range of applications involving small and large channels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.981

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 head0.009
GPT teacher head0.177
Teacher spread0.168 · 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 designSimulation or modeling
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

Citations10
Published2011
Admission routes2
Has abstractyes

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