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Record W7028925377

Glacial Dispersion at the Canadian Malartic Gold Deposit

2024· dissertation· en· W7028925377 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBedrockGlacial periodLithologyTrace elementPetrographyProspecting
DOInot available

Abstract

fetched live from OpenAlex

A novel drift prospecting approach detected components from previously established bedrock footprints at the world-class Canadian Malartic gold deposit within the site’s surrounding Quaternary sediments. The measured glacial dispersion of footprint components is significantly more extensive than the largest bedrock footprint. This new method could apply to similar high-tonnage disseminated gold deposits and other deposits with similar features. \nDrift prospecting methods included ice flow indicator mapping, surficial sediment sampling and characterization, particle size distribution analyses, till matrix geochemistry (major oxide, minor, and trace elements), glacial clast lithology, gold grain counts, and petrography. Our novel approach combined hyperspectral imaging analyses with petrographic analyses targeting glacial granules and pebbles. \nWithin the study area, the direction of past ice flow phases evolves from ~210º to ~150º; it dominates towards ~170º. Multivariate analysis of till matrix major oxide geochemistry links clusters of samples to bedrock geology and postglacial processes. Till clast lithologies link to bedrock type. The spatial distribution of till matrix minor and trace element geochemical values reveals glacial dispersal trains for Au, Ag, Rb, W, and potentially Ba; however, glacial dispersal trains for Cs, Mo, Pb, and Sr are unclear (all listed elements have been previously reported as footprint components). Multivariate analyses of till matrix minor and trace element geochemistry link clusters of samples to footprint components (Mo has the highest first principal component positive loading in the relevant cluster), bedrock geology background values, and postglacial processes. Sand-sized gold grain counts from till and their morphology link to the phengitic white mica bedrock footprint associated with the deposit. \nHyperspectral imaging analysis of bulk glacial clasts reveals dispersal extents that are significantly larger than the extent of the phengitic white mica footprint in bedrock (i.e, the most extensive bedrock footprint at the Canadian Malartic gold deposit). 4-8 mm tracer till clasts produce a dispersal extent 7.0-13.5 times larger while 2-4 mm tracer till clasts produce a dispersal extent 4.5-8.4 times larger than the bedrock footprint. Further, petrographic analyses of 42 highly phengitic 4-8 mm clasts classify 14 clasts as showing mineralogy and rock texture similar to the host quartz-monzodioritic, granodioritic, and meta-sedimentary rocks at the deposit. One of these clasts is mineralized with gold like the mineralization at the Canadian Malartic deposit, and this is the first reported instance of finding a mineralized clast using this novel technique. \nThe Quaternary dispersion relative to the previously established bedrock footprints at the Canadian Malartic gold deposit is significant. Ongoing hyperspectral imaging and petrographic analyses of similar clasts may demonstrate numerous practical applications to exploration campaigns seeking other Canadian Malartic-type gold deposits or other deposits with similar characteristics.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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