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Record W4390654194 · doi:10.1016/j.envpol.2024.123328

Legacy coal mining impacts downstream ecosystems for decades in the Canadian Rockies

2024· article· en· W4390654194 on OpenAlexaffabout
Colin A. Cooke, Craig A. Emmerton, Paul E. Drevnick

Bibliographic record

VenueEnvironmental Pollution · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsUniversity of CalgaryGovernment of AlbertaUniversity of Alberta
Fundersnot available
KeywordsCoal miningSedimentEnvironmental scienceHydrology (agriculture)Surface miningCoalMining engineeringGeologyArchaeologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Mountaintop removal coal mining leaves a legacy of disturbed landscapes and abandoned infrastructure with clear impacts on water resources; however, the intensity and persistence of this water pollution remains poorly characterized. Here we examined the downstream impacts of over a century of coal mining in the Crowsnest Pass (Alberta, Canada). Water samples were collected downstream of two historical coal mines: Tent Mountain and Grassy Mountain. Tent Mountain hosts a partially reclaimed surface mine that closed in 1983. Selenium concentrations downstream of Tent Mountain reached 185 μg/L in a lake below the mine spoil pile, and up to 23 μg/L in Crowsnest Creek, which drains the lake and the mine property. Further downstream, a well-dated sediment core from Crowsnest Lake records increases in sediment, selenium, lead, carbon, nitrogen, and polycyclic aromatic compounds that closely tracked the history of mining at Tent Mountain. In contrast, episodic discharge of mine water from abandoned underground adits at Grassy Mountain drive periodic (but short-term) increases in iron, various metals, and suspended sediment. These results underscore the lasting downstream impacts of abandoned and even reclaimed coal mines.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.880

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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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 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

Citations27
Published2024
Admission routes2
Has abstractyes

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