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Record W3167775759 · doi:10.5194/egusphere-egu21-8895

Opportunities related to Moroccan mine wastes valorisation

2021· article· en· W3167775759 on OpenAlexaff
Yassine Taha, Rachid Hakkou, Mostafa Benzaazoua

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsTailingsWaste managementHazardous wasteEnvironmental scienceMining engineeringEngineeringMetallurgyMaterials science

Abstract

fetched live from OpenAlex

<p>Large amounts of solid wastes are produced by the mining industry. These wastes are often considered as problematic materials as they can lead to harmful impacts on the surrounding environment and the society. However, it was proved by many studies that most of mine wastes are inert but sometimes mixed with problematic components such as sulfidic minerals and hazardous metals and metalloids. Reprocessing and retreatment of mine wastes is a key sustainable solution to remove the sources of pollution and to recover the remaining high value products. Many studies around the world have demonstrated the big interest in recovering the residual metals and the use of mine wastes in other applications such as the construction sector.</p><p> </p><p>In this study, a special accent will be given to the current management practices of mine wastes in Morocco as well as the possible opportunities related to the reuse of mine wastes coming from different mining activities. Three main materials categories are targeted: phosphate waste rocks and tailings, coal waste rocks and zinc tailings. The goal is to suggest more sustainable management methods and to explore new future opportunities related to the re-use and reprocessing of these wastes. Some possible high value-added products from these types of wastes are suggested based on their characteristics, location and volume. Construction aggregates, ceramics, bricks, cement, glass, acid mine drainage control, and road-base construction are among the possible explored channels.</p>

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

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)0.0010.000

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 head0.040
GPT teacher head0.239
Teacher spread0.199 · 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 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
Published2021
Admission routes1
Has abstractyes

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