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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 sur OpenAlexaboutno aff

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2021
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueScientific Research and Discoveries
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGlacial periodDispersion (optics)MineralTransition zoneMineral resource classificationAbundance (ecology)
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,464
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,004
Études des sciences et des technologies0,0040,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,256
Écart entre enseignants0,237 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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