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Record W4318619088 · doi:10.1080/17480930.2023.2170573

Prediction of the pervious surround performance of blast damage zone to reduce groundwater flow in backfilled open-pits

2023· article· en· W4318619088 on OpenAlexaff
Moïse Rousseau, Thomas Pabst

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2023
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsPolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsPermeability (electromagnetism)GroundwaterFlow (mathematics)Groundwater flowEnvironmental scienceGeotechnical engineeringGeologySoil scienceMechanicsAquiferChemistry

Abstract

fetched live from OpenAlex

Reducing groundwater flow in backfilled open-pit to limit interactions of the backfilled wastes with the environment often relies on the creation of preferential flow paths around the disposed wastes in the form of a pervious surround. The blast damage zone, which consists of an enhanced permeability zone near the pit walls, could naturally contribute to creating such preferential flow, thus eliminating the need to build a permeable envelope, reducing costs and maximising the volume for wastes deposition. The objective of this research was thus to propose parameters that could easily be accessed on the field, so practitioners could predict the flow deviation and evaluate if the BDZ is a sufficient containment structure to reduce interactions between backfilled wastes and the environment. Results showed the BDZ deviation could be predicted using the BDZ size and pit wall permeability only, and within a precision of ±15% without prior assumptions on the wastes or rock permeability. Three abacuses and one semi-analytical equation were proposed, and several criteria were derived to ensure the BDZ would act as a natural pervious surround. The results of these study should help mining operators and regulatory agencies to assess the BDZ effect on groundwater flow in backfilled open-pits.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.191

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)0.0000.000

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 head0.025
GPT teacher head0.225
Teacher spread0.200 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2023
Admission routes1
Has abstractyes

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