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Record W2079707101 · doi:10.1520/gtj100500

Detecting and Quantifying Leakage Through Defective Borehole Seals: A New Methodology and Laboratory Verification

2006· article· en· W2079707101 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.

Bibliographic record

VenueGeotechnical Testing Journal · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsPolytechnique MontréalSimon Fraser University
Fundersnot available
KeywordsBoreholePiezometerAquiferTRACERGeologyLeakage (economics)Geotechnical engineeringSeal (emblem)Sandbox (software development)Petroleum engineeringSoil scienceGroundwaterEngineering

Abstract

fetched live from OpenAlex

Abstract A new method for quantifying leakage through poorly sealed boreholes is presented and verified using a laboratory scale sandbox experiment. The method applies to a leaky borehole between two aquifers separated by an aquitard. A nonreactive tracer is injected into an upper aquifer piezometer, and the lower aquifer is pumped at a fixed rate. First, the presence of the tracer in the recovered water indicates the existence of the hydraulic short-circuit and cross-contamination. The leakage rate associated with the pumping rate can then be determined by measurement of the recovered tracer concentration. By correlating the leakage rate with the pumping rate, the hydraulic properties of the defective seal can be determined and the degree of cross-contamination can be predicted for any pumping rate. The method will be useful for practitioners who need to evaluate the quality of a borehole seal. The method is successfully tested using a laboratory sandbox experiment.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.728
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.109
GPT teacher head0.311
Teacher spread0.203 · 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