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Record W4285006405 · doi:10.22215/etd/2022-14987

Environmental processes that control the chemical recovery of arsenic impacted northern landscapes

2022· dissertation· en· W4285006405 on OpenAlexafffund
Michael H. Palmer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityW. Garfield Weston FoundationAurora Research Institute
KeywordsEnvironmental scienceEarth scienceSoil waterSedimentWatershedSTREAMSWeatheringPollutionGold miningArsenopyriteSurface waterHydrology (agriculture)GeologyGeochemistryEnvironmental engineeringEcologySoil scienceGeomorphology

Abstract

fetched live from OpenAlex

Arsenic (As) is a contaminant of global concern because of its toxic and carcinogenic properties.Waste streams associated with gold mining activities have resulted in extensive As pollution of landscapes worldwide.The Yellowknife area in the Northwest Territories, Canada was contaminated by more than half a century of As-bearing atmospheric mining emissions from the roasting of arsenopyrite ore during historical gold ore processing .The environmental footprint from legacy mining emissions persists on the landscape and questions remain about the distribution and fate of As in the local environment.The principal objective of this thesis was to improve our understanding of the environmental processes that control the chemical recovery of As impacted landscapes.Individual field studies were designed to address: a) the distribution, source, and solid-phase speciation of As in Yellowknife area soils; b) the seasonal variation of As in surface waters of shallow lakes; c) the influence of contrasting winter hydrology on the geochemical cycling of As in a shallow lake; and d) the seasonal contributions of terrestrial and aquatic fluxes of As in a contaminated watershed.The combination of mineralogical, geospatial, and statistical tools provided a novel method for determination of geochemical background in soils for areas impacted by legacy mining emissions, including recent unambiguous identification of anthropogenically impacted soils.Year-round investigations of surface waters, sediments, and sediment porewaters provided new insights into seasonal and under-ice processes that influence the mobility and geochemical cycling of As.The evaluation of terrestrial and aquatic As fluxes across a contaminated watershed revealed continued transport of legacy arsenic from catchment soils to lakes, and hydrology mediated within-lake processing and export of arsenic.Thus, climate and hydrology were found to play a fundamental role in the chemical recovery of As impacted lakes.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.000
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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