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Record W2807893173 · doi:10.7717/peerj.4850

What killed Frame Lake? A precautionary tale for urban planners

2018· article· en· W2807893173 on OpenAlexafffundabout
Melody J. Gavel, R. Timothy Patterson, Nawaf A. Nasser, Jennifer M. Galloway, Bruce W. Hanna, Peter A. Cott, Helen Roe, Hendrik Falck

Bibliographic record

VenuePeerJ · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsGovernment of Northwest TerritoriesWilfrid Laurier UniversityGeological Survey of CanadaCarleton University
FundersNatural Resources CanadaUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaQueen's UniversityQueen's University BelfastTides CanadaRoyal Bank of CanadaPolar Knowledge Canada
KeywordsUrbanizationBioindicatorWater qualityEnvironmental remediationEnvironmental scienceHydrology (agriculture)Physical geographyGeographyContaminationGeologyEcology

Abstract

fetched live from OpenAlex

Frame Lake, located within the city of Yellowknife, Northwest Territories, Canada, has been identified as requiring significant remediation due to its steadily declining water quality and inability to support fish by the 1970s. Former gold mining operations and urbanization around the lake have been suspected as probable causes for the decline in water quality. While these land-use activities are well documented, little information is available regarding their impact on the lake itself. For this reason, Arcellinida, a group of shelled protozoans known to be reliable bioindicators of land-use change, were used to develop a hydroecological history of the lake. The purpose of this study was to use Arcellinida to: (1) document the contamination history of the lake, particularly related to arsenic (As) associated with aerial deposition from mine roaster stacks; (2) track the progress of water quality deterioration in Frame Lake related to mining, urbanization and other activities; and (3) identify any evidence of natural remediation within the lake. Arcellinida assemblages were assessed at 1-cm intervals through the upper 30 cm of a freeze core obtained from Frame Lake. The assemblages were statistically compared to geochemical and loss-on-ignition results from the core to document the contamination and degradation of conditions in the lake. The chronology of limnological changes recorded in the lake sediments were derived from 210 Pb, 14 C dating and known stratigraphic events. The progress of urbanization near the lake was tracked using aerial photography. Using Spearman correlations, the five most significant environmental variables impacting Arcellinida distribution were identified as minerogenics, organics, As, iron and mercury ( p < 0.05; n = 30). Based on CONISS and ANOSIM analysis, three Arcellinida assemblages are identified. These include the Baseline Limnological Conditions Assemblage (BLCA), ranging from 17–30 cm and deposited in the early Holocene >7,000 years before present; the As Contamination Assemblage (ACA), ranging from 7–16 cm, deposited after ∼1962 when sedimentation began in the lake again following a long hiatus that spanned to the early Holocene; and the Eutrophication Assemblage (EA), ranging from 1–6 cm, comprised of sediments deposited after 1990 following the cessation of As and other metal contaminations. The EA developed in response to nutrient-rich waters entering the lake derived from the urbanization of the lake catchment and a reduction in lake circulation associated with the development at the lake outlet of a major road, later replaced by a causeway with rarely open sluiceways. The eutrophic condition currently charactering the lake—as evidenced by a population explosion of eutrophication indicator taxa Cucurbitella tricuspis —likely led to a massive increase in macrophyte growth and winter fish-kills. This ecological shift ultimately led to a system dominated by Hirudinea (leeches) and cessation of the lake as a recreational area.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.998

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)0.0080.003

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 head0.259
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations23
Published2018
Admission routes3
Has abstractyes

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