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Record W2169284050 · doi:10.1680/gein.2007.14.4.219

Quantifying geomembrane wrinkles using aerial photography and digital image processing

2007· article· en· W2169284050 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeosynthetics International · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeomembraneWrinklePhotographyGeosyntheticsGeotechnical engineeringGeologyEnvironmental scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Geomembranes are highly effective barriers, and are often a key component in the design of composite landfill liner and cover systems. During installation, solar exposure causes some types of geomembrane to buckle locally upwards and form networks of wrinkles (sometimes referred to as waves). These wrinkle networks may be significant in terms of increasing leakage through this barrier system if there is a hole at or near the wrinkle. In this paper, a novel method to quantify geomembrane wrinkles in the field is reported using low-altitude aerial digital photography and image processing techniques. The results of the analysis indicate that, at the date and time the aerial image was captured, the geomembrane contained 100 major wrinkles, which covered 13.9% of the total area of the exposed geomembrane. More importantly from a potential leakage perspective, over 90% of these wrinkles were found to be hydraulically connected over the entire field of view of the exposed geomembrane. This one hydraulically connected wrinkle was found to have an aggregate length of 520 m.

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 score0.186
Threshold uncertainty score0.714

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)0.0010.000

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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