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Record W4200387427 · doi:10.1139/cgj-2021-0346

Hydraulic conductivity testing and destructive sampling of field-scale mine waste test piles

2021· article· en· W4200387427 on OpenAlexvenueno aff
Mohammad R. H. Gorakhki, Christopher A. Bareither, Joseph Scalia

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic conductivityPileGeotechnical engineeringTailingsGeologySoil waterMaterials scienceSoil science

Abstract

fetched live from OpenAlex

A commingled waste rock and tailings test pile and a waste rock test pile were evaluated to determine saturated hydraulic conductivity and destructively sampled to measure dry density. The commingled test pile contained a mixture of filtered tailings and waste rock blended to isolate waste rock particles as inclusions within the tailings matrix. Test piles were constructed in the shape of truncated 5 m high pyramids with 25 m base sides and flat 5 m × 5 m top surfaces, and instrumented to monitor water content (and additional geochemical indicator parameters) within the test pile and seepage from the base of the pile. Piles were decommissioned after 26 months of operation. Saturated hydraulic conductivities were measured using sealed double-ring infiltrometers (2.4 m square outer ring and 1 m square inner ring). Tensiometers and embedded water content sensors were used to measure progression of the wetting front, and the final location of the wetting front in the commingled test pile was directly measured during decommissioning. Field-measured saturated hydraulic conductivities were compared to laboratory-measured results intended to simulate the test piles. Despite having a lower average density, the commingled waste rock and tailings had a hydraulic conductivity approximately 2.5 times lower than the waste rock.

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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