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Record W4285161130 · doi:10.11159/ijci.2022.005

An Insight into the Hydraulic Response of Unsaturated Sand-Silt Mixtures of Stava Tailings

2022· article· en· W4285161130 on OpenAlexvenueno aff
Gianluca Di Bella

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2022
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
FundersPolitecnico di Torino
KeywordsTailingsSiltGeologyGeotechnical engineeringGeochemistryGeomorphologyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

This research provides the main outcomes of an experimental investigation into the water retention behaviour of unsaturated tailing wastes collected after the failure of the Stava tailing dams. The water retention response of Stava tailings is studied by carrying out tests which apply different techniques, respectively the axis translation method, the vapour equilibrium technique and the dew-point method. The dependency of the Water Retention Curve (WRC), relative hydraulic conductivity and apparent cohesion on the grain size distribution, and on the initial void ratio was investigated to account the in-situ heterogeneity of tailing wastes. As for standard soils, looser tailing specimens showed a lower water retention capability than that given in the denser ones. Similarly, the reduction of the fine content showed to decrease in the water retention capability of tailings. A microstructural interpretation in terms of the cumulative value of the pore size density (PSD) is also provided. The in-depth knowledge of the hydro-mechanical response of the soils proposed by the current research finds its practical application as a fundamental tool to reliably assess the stability of the tailing storage facilities which the high rate of recent collapses poses unacceptable fatalities with environmental and economic damages.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.572

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 science0.0010.000
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 head0.006
GPT teacher head0.220
Teacher spread0.215 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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