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Record W7017846188

Chemostratigraphy and structural framework for gold mineralization at the Goliath Deposit, Western Wabigoon Subprovince, Ontario

2022· dissertation· en· W7017846188 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMineralization (soil science)FelsicPyriteStockworkLineationSedimentary rockVolcanic rockOverprintingMetallogenyLode
DOInot available

Abstract

fetched live from OpenAlex

The Goliath deposit (32 Mt at 1.09 g/t Au and 3.42 g/t Ag) is one of the larger gold deposits within the western Wabigoon subprovince, 20 km east of Dryden, Ontario. The economic potential of the Goliath deposit makes it important to understand its geological setting to improve exploration models for such significant targets. Felsic volcanic sedimentary rocks (maximum age of ~ 2703 Ma) host the mineralization, were sericitized, and metamorphosed into the muscovite- sericite schist and biotite-muscovite schist. This package is enclosed within a similarly aged turbidite sequence (maximum age of ~2701 Ma). The mineralization consists of base metal sulphides with gold and silver hosted in As-rich pyrite and remobilized along pyrite fractures. The mineralization is likely pre-deformation as the regional compressional D1 deformation and transpressional D2 deformation reoriented the grade shells to be subparallel to the S1 foliation (075°/78°), and higher-grade shells subparallel to the intersection lineation of S1 and S2 fabric (52°/218°), and subparallel to the F1 fold axial plane (28°/81°). The Goliath gold deposit is thus interpreted to have formed in a synvolcanic, pre-orogenic environment.

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 score0.020
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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
Published2022
Admission routes1
Has abstractyes

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