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Record W2075900915 · doi:10.1139/t99-086

Tire-reinforced earthfill. Part 3: Environmental assessment

2000· article· en· W2075900915 on OpenAlexvenueno aff
Vince O'Shaughnessy, Vinod K. Garga

Bibliographic record

VenueCanadian Geotechnical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsScrapEnvironmental scienceLysimeterGeotechnical engineeringGeomembraneContaminationGroundwaterDrainageEffluentWaste managementWater qualityEnvironmental engineeringEngineeringMaterials scienceSoil waterMetallurgy

Abstract

fetched live from OpenAlex

A reinforced earthfill was constructed using approximately 10 000 scrap tires. Water samples were collected from a drainage system installed below the tire-reinforced earthfill and analyzed for chemical quality. Additional tests on water quality were performed in laboratory test columns in which tire chips were embedded in sand or clay to provide a conservative estimate of the potential release of toxic compounds. Field monitoring of the effluent indicated that no significant adverse effects on groundwater quality had occurred over a period of 2 years. Laboratory batch tests performed on tire chips embedded in sand provided evidence of an increase in solution of certain metal elements which in some cases exceeds their respective drinking-water standards. This increase was attributed to the exposed steel reinforcement found in the tire chips. The amount of organic compounds leached from the tire chips decreased with the number of exposure periods or pore volumes flushed through the soil.Key words: whole scrap tires, water quality, tire chips, field investigation, laboratory lysimeter tests.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.174
Teacher spread0.169 · 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 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

Citations53
Published2000
Admission routes1
Has abstractyes

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