MétaCan
Menu
Back to cohort
Record W2136256226 · doi:10.1002/ieam.1616

Reconstructing the ecological impacts of eight decades of mining, metallurgical, and municipal activities on a small boreal lake in northern Canada

2015· article· en· W2136256226 on OpenAlexafffundabout
Lorne E. Doig, Stephanie T Schiffer, Karsten Liber

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsPaleolimnologySedimentTailingsChironomidaeEcologyEnvironmental scienceDiatomLake ecosystemPost-industrial societyEffluentSubfossilGeologyOceanographyHoloceneEcosystemChemistryEnvironmental engineeringBiologyPaleontology

Abstract

fetched live from OpenAlex

Abstract As a result of long-term metal mining and metallurgical activities, the sediment of Ross Lake (Flin Flon, MB, Canada) is highly contaminated with metals and other elements. Although the effluents likely were discharged into Ross Lake as early as the late 1920s, lake biophysical data were not collected until 1973, more than 4 decades after the onset of mining and municipal activities. The early influence of these activities on the ecology of Ross Lake is unknown, as are the effects of improvements to metallurgical effluent quality and discontinuation of municipal wastewater discharge into the lake's north basin. To address this knowledge gap, analyses typical of paleolimnological investigations were applied to cores of sediment collected in 2009 from the south basin of Ross Lake. Stratigraphic analyses of physicochemical sediment characteristics (e.g., the concentrations of metals and other elements, organic C, total N, and δ13C and δ15N values) and subfossil remains (diatoms, Chironomidae, Chaoborus, and Cladocera) were used to infer historical biological and chemical changes in Ross Lake. With the onset of mining activities, concentrations of various elements (e.g., As, Cr, Cu, Zn, and Se) increased dramatically in the sediment profile, eventually declining with improved tailings management. Nevertheless, concentrations of metals in recent sediments remain elevated compared with pre-industrial sediments. Constrained cluster analyses demonstrated distinct pre-industrial and postindustrial communities for both the diatoms and chironomids. The biodiversity of the postindustrial diatom assemblages were much reduced compared with the pre-industrial assemblages. The postindustrial chironomid assemblage was dominated by Chironomus and to a lesser extent by Procladius, suggesting that Ross Lake became a degraded environment. Abundances of Cladocera and Chaoborus were severely reduced in the postindustrial era, likely because of metals toxicity. Overall, improvements to the management of both metallurgical and municipal effluent are reflected in the physicochemical sediment record; nevertheless, the ecology of Ross Lake remains impaired and shows minimal signs of returning to a pre-industrial state. Recommendations are made regarding possible future investigations at this site and the need for a framework to help assess causation using paleolimnological and other site data. Integr Environ Assess Manag 2015;11:490–501. © 2015 SETAC Key Points Ross Lake became grossly contaminated with various trace elements shortly after the onset of metallurgical and municipal activities. Various stressors associated with mining operations and municipal sewage discharge have acted individually or in combination to fundamentally alter the ecology of Ross Lake in the postindustrial era. Improvements have been made to mine effluent quality and the location of municipal sewage discharge; nevertheless, the ecology of Ross Lake remains impaired and shows minimal signs of recovery. Recommendations are made regarding possible future investigations at this site and the need for a framework to help assess causation using paleolimnological and other site data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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

Citations28
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueIntegrated Environmental Assessment and ManagementSame topicHeavy metals in environmentFrench-language works237,207