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Record W4393689201 · doi:10.5281/zenodo.5644208

Numerical simulation of groundwater flow in backfilled open-pits for BDZ deviation performance

2021· dataset· en· W4393689201 on OpenAlexaboutno aff
Moïse Rousseau, Thomas Pabst

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterGroundwater flowFlow (mathematics)Environmental scienceGeologyHydrology (agriculture)Computer scienceGeotechnical engineeringMathematicsAquiferGeometry

Abstract

fetched live from OpenAlex

This dataset contains simulated groundwater flow entering multiple backfilled open-pits with mine wastes (flowrate results) and some Python script to visualize the data.<br> The objective was to predict the blast damage zone (BDZ) created by mine excavation on the flowrate through the backfilled wastes.<br> Flowrates were obtained by 3D numerical simulation using PFLOTRAN finite volume code (www.pflotran.org).<br> Conceptual model of the BDZ and equivalent permeability was derived from Rousseau and Pabst (in press) and Mourzencko et al. (2012) (see https://hal.archives-ouvertes.fr/hal-01196684/file/mourzenko2012.pdf).<br> Number of cases simulated: 40500, with 40496 converged numerical simulations.<br> Calculated were performed using the High Performance Computing Ressource of Calcul Quebec (www.calculquebec.ca/) and Compute Canada (www.computecanada.ca/)

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.361
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.236
Teacher spread0.209 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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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