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Record W2806148280 · doi:10.1088/1748-9326/aaca9d

The influence of mining on hydrology and solute transport in the Elk Valley, British Columbia, Canada

2018· article· en· W2806148280 on OpenAlexafffundabout
Christopher Wellen, Nadine J. Shatilla, Sean K. Carey

Bibliographic record

VenueEnvironmental Research Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWeatheringHydrology (agriculture)LimitingDrainage basinWater qualityDominance (genetics)Environmental scienceStructural basinSurface waterGeologyGeochemistryEnvironmental engineeringGeomorphologyGeographyEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Previous research has established that surface mining affects downstream water quality. However, there remains limited information and understanding about the interaction of hydrology and geochemistry in surface mined areas. This paper presents an analysis of a multi-year dataset of geochemistry across a gradient of surface mining in an alpine environment. This work formed part of an R&D program examining the influence of mining on hydrological and water quality responses in the Elk Valley, British Columbia, Canada, aimed at informing effective management responses. Results indicate that water from waste rock dumps has a consistent ionic profile that is distinct from reference catchments. The export of weathering solutes did increase with the degree of mine affected area, and was consistently limited more strongly by transport capacity than by supply. Geographical location of waste rock within the catchment (headwaters or outlet) did not affect chemical concentrations or the timing with which chemically distinct waters reported to basin outlets. The dominance of transport capacity over source as limiting to solute export highlights the importance of limiting water input to waste rock piles. However, results strongly suggest that lateral water inputs do not mobilize significant amounts of weathering solutes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations33
Published2018
Admission routes3
Has abstractyes

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