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Errors with Small Volume Elastic Seepage Meter Bags

2002· article· en· W2127341506 on OpenAlexaff
Robert A. Schincariol, John D. McNeil

Bibliographic record

VenueGround Water · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsWestern University
Fundersnot available
KeywordsVolume (thermodynamics)Elasticity (physics)Relaxation (psychology)MetreMechanicsMaterials scienceData collectionComposite materialPhysicsThermodynamicsStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

The use of small volume elastic collection bags (condoms) has become popular in seepage meter studies in recent years, despite minimal field or laboratory validation of their use and, specifically, the impact of their elasticity on seepage measurements. A laboratory study was initiated after field results using small elastic collection bags produced seepage data that did not correlate with hydrometric data. The laboratory data demonstrate that condoms undergo significant mechanical relaxation during seepage measurement times typically observed in field settings. Unlike conventional nonelastic collection bags, which mechanically relax over several minutes, the condoms suffered from a slow mechanical relaxation or equilibration. Over nine hours, condoms gained 43 mL of water, approximately 50% of maximum workable volume (between mechanical relaxation effect and elastic limit), under stagnant flow conditions. This long-term equilibration invalidates simple subtraction of equilibration volumes from collection volumes as a correction technique. Previously published studies using flexible small-volume elastic measurement bags (condoms) have not reported a mechanical relaxation effect. Overall, because the condom's small workable volume and inherent variability, we would not recommend any small-volume elastic measurement bags for quantitative seepage measurements.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.992

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)0.0090.010

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 head0.018
GPT teacher head0.178
Teacher spread0.160 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2002
Admission routes1
Has abstractyes

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