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Record W3206456104 · doi:10.1144/geochem2021-057

The effect of filtration size on the geochemistry of groundwater samples from a massive sulfide deposit at the Bathurst Mining Camp, New Brunswick, Canada

2021· article· en· W3206456104 on OpenAlexaffabout
Derek R. Knaack, Gillian Ivey, Caitlyn MacPhee, Jordan Peterzon, Liam M. Price, Matthew R. Williams, Matthew I. Leybourne

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
Fundersnot available
KeywordsGroundwaterSulfideGeologyGeochemistrySulfide mineralsMining engineeringFiltration (mathematics)ArchaeologyEnvironmental scienceChemistryGeographyPyriteGeotechnical engineering

Abstract

fetched live from OpenAlex

In hydrogeochemical studies, samples are commonly filtered to limit the fraction of analyte that is adsorbed or structurally bound to suspended particles, ensuring that only the dissolved fraction is analysed, and thereby reducing analytical bias during measurement. The standard filter size that has been adopted is 0.45 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m; however, ultrafiltration can be used to remove colloidal particles two orders of magnitude smaller. In the following, we investigate the effect that standard filtration (0.45 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m) and ultrafiltration (0.004 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m) have on the hydrogeochemistry of groundwaters from a volcanogenic massive sulfide (VMS) deposit at the Bathurst Mining Camp, New Brunswick, Canada. Groundwater samples were collected from six monitoring wells at the Nigadoo Mine tailings facility, and major and trace geochemistry were determined using a combination of inductively coupled plasma optical emission spectrometry, inductively coupled plasma mass spectrometry and ion chromatography. Waters at the Nigadoo deposit are generally enriched in Ca and SO 4 , relative to other major cations and anions. Some element contents – including those associated with VMS deposits – differ depending on the filtration technique used (e.g. As, Fe, Pb, rare earth elements and yttrium (REY)), some are equally affected by both techniques (e.g. Cu, Ni, Zn), and some are unaffected by filtration (e.g. Ba, Ca, Mn, Cl − ). Shale-normalized REY anomalies (Ce SN /Ce SN *, Eu SN /Eu SN * and Y SN /Ho SN ) and overall patterns can differ greatly (e.g. changing the sign of the anomaly) depending on the filtration technique used. We observe previously undocumented and, at this time, unexplainable fractionation of Ho and Yb (non-redox sensitive REY, unaffected by the tetrad effect) in unfiltered waters from the Nigadoo deposit. Differences in groundwater geochemistry induced by filtration technique can result in false positive and negative anomalies during environmental and exploration projects and must therefore be carefully considered. At the Nigadoo site, oxidation of sulfide minerals can occur, resulting in the formation of relatively unstable oxide minerals. Away from the tailings, where carbonate minerals are scarce and can no longer act as a pH buffer, the unstable oxide minerals break down and release metals and metalloids into the surrounding environment. The filtration methods used in this study can provide insight into where the specific metals and metalloids are hosted and how they are likely to behave under different redox conditions. Because VMS deposit pathfinder elements are enriched in unfiltered water, and differ by degree of filtration, geochemical analysis of the filtride material may also make an effective exploration tool. Thematic collection: This article is part of the Hydrochemistry related to exploration and environmental issues collection available at: https://www.lyellcollection.org/cc/hydrochemistry-related-to-exploration-and-environmental-issues

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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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.990

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.008
GPT teacher head0.168
Teacher spread0.160 · 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.

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

Citations2
Published2021
Admission routes2
Has abstractyes

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