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Record W2159991007 · doi:10.1139/s05-008

Effects of rainfall events on waste-rock surface-water conditions and CO2 effluxes across the surfaces of two waste-rock piles

2005· article· en· W2159991007 on OpenAlexfundvenueaboutno aff
Louis Kabwe, G. Ward Wilson, M. Jim Hendry

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceHydrology (agriculture)Surface waterGeologyMining engineeringEnvironmental engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

The influence of rainfall events on the surface-water conditions of waste rock and the resultant CO2 gas effluxes from waste-rock piles are of value in the development of a long-term management plan for these piles. However, data for the direct measurement of CO2 effluxes through field-scale waste-rock piles are lacking. This study investigated the influence of a short-term, multi-day (29 July to 5 August 2002) heavy rainfall event on waste-rock water conditions and CO2 effluxing from two large waste-rock piles at the Key Lake uranium mine in northern Saskatchewan. The study also investigated spatial and temporal variation in CO2 effluxes in these waste-rock piles over a 2-year period (summer 2000 – summer 2002). Results showed that the impact of a heavy rainfall event on surface-water conditions and CO2 effluxes from these waste-rock piles is of relatively short duration. Results also showed that the CO2 effluxes were relatively uniform, both spatially and temporally (average covariance (CV) is 28%–39%), over the 2-year test period. This information can provide an important tool in the development of a long-term management plan for mine waste-rock piles. Key words: CO2 efflux, waste-rock piles, rainfall events, unsaturated zone.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.329

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)0.0000.000

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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Citations3
Published2005
Admission routes3
Has abstractyes

Explore more

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