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Record W7039941951

Numerical Study of Sediment and Chemical Transport in the Lower Athabasca River Due to a Tailings Dam Breach Spill and in the Context of Climate Change

2022· other· fr· W7039941951 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2022
Typeother
Languagefr
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Tailings damFlood mythHydrology (agriculture)Tailings
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Les barrages de résidus sont connus pour causer des accidents et ont déjà été la cause d'accidents mortels. Maintenant que cette question est posée, quelles seront les conséquences de la rupture possible des digues à résidus, et ces effets persisteront-ils au fil du temps ? Ces barrages offrent la possibilité de stocker à grande échelle et pour une longue durée les déchets issus du processus d'extraction minière. Rien qu'au cours du siècle actuel, qui s'est écoulé depuis près de 2 décennies, 11 ruptures graves de ces barrages ont été signalées, et il semble que ce nombre soit en augmentation. Dans le même temps, la probabilité de défaillance et d'effondrement des digues à résidus est plus élevée que dans les barrages hydrauliques, et cela s'explique par leur utilisation industrielle. Les barrages hydrauliques sont des structures fiables qui sont utilisées pour stocker l'eau et être efficaces dans son utilisation, tandis que les barrages à résidus sont utilisés pour stocker les résidus qui ne sont pas nécessaires. En conséquence, nous préférons dépenser le moins d'argent pour leur construction. Pour répondre à la question de savoir pourquoi une digue à résidus s'effondre, plusieurs raisons peuvent être avancées, telles que le poids imposé à la digue à la suite de fortes pluies et la faiblesse des fondations de la digue. De plus, la transformation des déchets solides en une substance liquide peut entraîner le rejet de déchets à la surface de l'eau et dans les eaux souterraines, ce qui constitue une menace pour la faune. ABSTRACT: Tailings dams are notorious for causing accidents and have been the cause of fatal accidents before. Now this question is raised, what consequences will befall us due to the possible failure of tailings dams, and will these effects still remain over time? These dams provide the possibility of storing wastes from the mineral extraction process on a large scale and for a long time. Only in the current century, which has passed nearly 2 decades, 11 serious breaks in these dams have been reported, and it seems that this number is increasing. At the same time, the probability of failure and collapse of tailings dams are higher than in water dams, and the reason for this is their industrial use. Water dams are reliable structures that are used to store water and be efficient in its use, while tailings dams are used to store residues that are not needed. As a result, we prefer to spend the least amount of money on their construction. To answer the question of why a tailings dam collapses, several reasons can be put forward, such as the weight that is imposed on the dam as a result of heavy rains and the weak foundation of the dam. Also, the transformation of solid waste into a liquid substance can lead to the discharge of waste to the water surface and underground water, which is a threat to wildlife. Many historic catastrophic tailings dams failures in Canada have raised concerns about the risk associated with oil-sands tailings, such as safety and environmental impacts. Therefore, it has increased the interest to investigate the potential effects of the failure of an oil sand tailings dam on the water quality as well as the adjacent lands in the downstream area. Studying and modeling the flow after the failure of tailings dams creates its own difficulties due to the non-Newtonian behavior of tailings materials. This study aims to numerically investigate the flow of dam breach tailings on oil sands tailings dams.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 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
Published2022
Admission routes1
Has abstractyes

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