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Use of near-surface waters in identifying elemental associations with geothermal-sourced Li

2025· article· en· W4410458460 on OpenAlexafffund
Andrew Robinson, Gavin Stewart, Brendan Bishop, Leslie J. Robbins, Shannon L. Flynn

Bibliographic record

VenueApplied Geochemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Regina
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaNewcastle University
KeywordsGeothermal gradientGeologyEnvironmental scienceGeochemistryEarth scienceMining engineeringMineralogyPaleontology

Abstract

fetched live from OpenAlex

Unprecedented demand for lithium (Li) is being driven by its use in electric vehicle batteries. Currently, the majority of Li comes from pegmatite mining and salar brines, however, new sources such as geothermal brines will be required to meet future demand. The North Pennines, Northern England has been found to host brines with lithium concentrations exceeding 90 mg/L from 411 to 995 m. However, deep subsurface water chemistry for the region is limited to a single abandoned borehole, necessitating the use of other techniques in assessing the resource potential of these brines. This work investigated the potential of surface and near-surface water samples from abandoned mine workings to expand the known geographic extent of the underlying Li brine resource. Li concentrations were 1.9 to 784 μg/L at the 44 locations sampled. Principal component, cluster, and covariate analyses identified two distinct water chemistry clusters mostly related to dimension 1 of the PCA (22.5% of variance) and included alkalinity, Ca, Cd, Cl, F, K, Li, Mg, Na, Se, and SO 4 2- ; the “near surface” or potentially orebody related group which included Al, As, Cu, Eu, Fe, P, Pb, V, and Y. Two smaller clusters are present on the positive and negative axis of dimension two (14.1%); on the positive is B, Ba, Br, Cr, pH, and Si, and on the negative, Co, Mn, Ni, Sc, Sr, and Zn. The Cambokeels Mine, 0.5 km from the original borehole, had the highest Li concentration of 78.4 mg/L. However, the deep brine signature and Li enrichment were also found at a cluster of mines 15 km away, significantly expanding the geographical extent of the North Pennine Li brine resource. These findings show that relatively low-cost elemental analysis and statistical analyses could be a promising exploration tool for regions where there is limited data. Developing tools using geochemical finger printing of near-surface waters to identify Li resources in deeper geothermal brines will be essential for the cost-effective development of critical minerals. • Near-surface measurements may be useful in assessing geothermal resources. • North Pennine geothermal Li is likely sourced from the Weardale Granite. • Li associated with alkalinity, Ca, Cd, Cl, F, K, Mg, Na, Se, and SO 4 in mine water. • Statistical analysis is a low cost first step before expensive drilling.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.370

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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