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Record W4214925078 · doi:10.3390/geosciences12030114

Zircon from Altered Monzonite Rocks Provides Insights into Magmatic and Mineralizing Processes at the Douay Au Project, Abitibi Greenstone Belt

2022· article· en· W4214925078 on OpenAlexafffund
Lucie Mathieu, Taylor D. Wasuita, Ross Sherlock, Fred Speidel, Jeffrey Marsh, B Dubé, O Côté-Mantha

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsAgnico Eagle (Canada)Geological Survey of CanadaLaurentian UniversityUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsZirconQuartz monzoniteGeochemistryGreenstone beltGeologyHydrothermal circulationQuartzArcheanPlutonTectonics

Abstract

fetched live from OpenAlex

Zircon provides essential information on the age and oxidation state of magmatic systems and can be used to characterize magmatic-hydrothermal Au mineralizing systems. Using the Douay intrusion-related gold system (IRGS) as a type example of Neoarchean syenite-associated mineralization (Abitibi greenstone belt), we demonstrate that zircon from altered quartz-monzonite rocks can also be used to infer the age of a magmatic-hydrothermal event. Here, zircon chemistry is used to identify the following sequence of events at the Douay exploration project: (1) the crystallization of zircon at ~2690 Ma in evolved residual melts with distinct U-contents (quartz-monzonite magma); (2) the extensive radiation damage for the U-rich grains over a period of ~10–15 My; and (3) the alteration of zircon grains at ~2676 Ma by interaction with magmatic-hydrothermal mineralizing fluids derived from syenite and carbonatite intrusive phases. This study also distinguishes extensively altered zircon grains from pristine to least-altered zircon formed in distinct magmatic environments using a Th/U vs. U discrimination diagram.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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