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Record W7001003352

Hunting for Rare-Earth-Element (REE)-bearing minerals
\nin Northern Labrador: MLA-SEM analysis of surficial
\nsediments within the glacial dispersion zone from the
\nStrange Lake main zone deposit

2021· dissertation· en· W7001003352 on OpenAlexaboutno aff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsnot available
Fundersnot available
KeywordsGlacial periodDispersion (optics)MineralTransition zoneMineral resource classificationAbundance (ecology)
DOInot available

Abstract

fetched live from OpenAlex

The Strange Lake area hosts important Zr-Nb-Y-REE deposits, associated with a small peralkaline
\ngranite intrusion. The deposits and the host rocks contain unusual minerals, some of which are
\nessentially unique to this site. Geochemical data from glacial sediments or “tills” define dispersion from
\nthe deposits for at least 35 km, and the Strange Lake area is regarded as a “type example” of linear
\nglacial dispersion from a point source. This thesis study uses Mineral Liberation Analysis – Scanning
\nElectron Microscopy (MLA-SEM) methods to investigate the mineralogy of glacial sediments and
\ndocument the dispersion of unusual (indicator) minerals. It is in part an assessment of the MLA-SEM
\ntechnique for use in indicator-mineral studies, which are increasingly important in mineral exploration.
\nSeventy-six samples of till were collected from an area extending for 35 km ENE of the Strange
\nLake Main Zone deposit, aligned with the inferred direction of ice movement. Samples were processed
\nto separate the 0.125 - 0.18 mm size fraction for direct analysis, without any preferential separation of
\ndenser minerals. MLA-SEM results thus directly document the abundances of 55 minerals, ranging from
\ncommon silicates to rare accessory minerals diagnostic of the Strange Lake deposits. This large database
\nwas then evaluated using statistical and geographical analysis methods. Common silicates (e.g., quartz,
\nfeldspars, garnet and amphiboles) collectively make up > 90% of typical till samples, but the rarest
\nindicator minerals occur at levels < 10 ppm. The reliability of data degrades at such low abundances (in
\npart due to probability effects) but systematic geographic variation patterns can still be discerned for
\nmany such rare minerals. Numerous diagnostic minerals from Strange Lake were detected, although
\ntheir abundance was lower than expected from previous MLA-SEM analyses of drill core samples.
\nMany minerals show linked abundance variation (correlation or anti-correlation) and such
\nvariation commonly has a geographic component. Systematic geographic variations for major minerals
\nand many minor minerals seem to correspond with regional contrasts in bedrock geology from west to
\neast, suggesting that patterns mostly record local provenance. Accessory minerals that are diagnostic of
\nStrange Lake also show systematic geographic abundance variations, which are superimposed on these
\nregional trends, but in some cases the patterns appear superficially similar. The most abundant and
\npersistent indicator minerals are the Ca-Zr silicate gittinsite and the Y-Ca-REE silicate gerenite, which are
\nalso the most abundant in the Strange Lake source rocks. However, geographic variation patterns for
\nthese minerals are rather different. Gerenite abundance diminishes in a down-ice direction, as expected,
\nbut gittinsite seems to increase in abundance, which is unexpected. Other indicator minerals mostly
\ndiminish in abundance in a down-ice direction but even some of the rarest (e.g., stetindite, gadolinite
\nand bastnaesite) remain sporadically detectable at 35 km from the source. The controls on dispersion
\npatterns are not fully understood, but likely involve mineralogical factors as well as aspects of the glacial
\nenvironment. MLA-SEM data suggest that many indicator minerals from Strange Lake typically form
\nsmall domains within larger particles of common minerals, and these host minerals may thus influence
\ndispersion patterns. In this context, it is interesting that the most persistent indicator minerals seem to
\nbe preferentially associated with quartz, which is the most durable of common rock-forming minerals.
\nLike most research studies, this project did not answer all questions posed at the outset, and it
\ndid not always follow the intended plan. However, the results indicate that the MLA-SEM method has
\nconsiderable potential for use in indicator-mineral studies, and point to interesting future research
\ndirections connected to development of the method and its application to other geological problems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 designQualitative
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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