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Record W2033762859 · doi:10.1520/gtj11097j

A Cyclic Gradient Ratio Test Device

2002· article· en· W2033762859 on OpenAlexaff
A Hameiri, R. J. Fannin

Bibliographic record

VenueGeotechnical Testing Journal · 2002
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHydraulic headOutflowInflowMechanicsFlow (mathematics)Flow conditionsVolumetric flow rateHead (geology)Materials scienceGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract Gradient ratio test results are reported from the commissioning of a device that was modified to impose dynamic cyclic flow. The flow conditions, using head control, are similar to those in marine protection works. Each test included a stage of unidirectional flow, followed by stages of cyclic flow at a frequency of 0.2 Hz and 0.02 Hz. Flow reversal invokes an initial transient state of water head distribution in the sample, attributed to changes in hydraulic gradient to accommodate the new values of constant head at the inflow and outflow. Consequently, tests at the higher frequency yielded unsteady flow in the sample, while tests at the lower frequency resulted in steady flow during each half cycle. In unsteady flow, the transient state of water head distribution (and hence gradient ratio) cannot serve as an index of soil-geotextile compatibility. Therefore, a recommendation is made that testing of filtration compatibility in dynamic cyclic flow include a stage of slow flow reversal (or alternatively unidirectional flow) to permit a quantification of water head distribution under steady flow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.034
GPT teacher head0.214
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2002
Admission routes1
Has abstractyes

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