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Record W4362579595 · doi:10.1190/tle42040237.1

A comparison of airborne geophysical data over two magmatic nickel deposits

2023· article· en· W4362579595 on OpenAlexaffabout
Hannah N. Peterson

Bibliographic record

VenueThe Leading Edge · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCondor Petroleum (Canada)
Fundersnot available
KeywordsMineralization (soil science)GeologySulfideGeochemistryNickelExtensional definitionVolcanogenic massive sulfide ore depositMineralogySeismologyTectonicsMetallurgyMaterials scienceSoil science

Abstract

fetched live from OpenAlex

Abstract Historical exploration for economic nickel (Ni) mineralization has often targeted magmatic sulfide deposits in extensional settings. However, convergent-margin-hosted Alaskan-type complexes represent a potentially underexplored source of Ni. Case studies of the geophysical responses associated with two magmatic Ni deposits (one is typical, and one is associated with an Alaskan-type complex) are presented, and the results are compared. Data were assessed from historical and newly acquired airborne geophysical surveys that were collected over the Mayville property in southeast Manitoba and the Turnagain property in northern British Columbia. The properties were explored by Mustang Minerals Corporation and Giga Metals Corporation, respectively. Airborne electromagnetic (EM) and magnetic data were utilized to compare the two properties and the mineralized zones. The review showed that the Mayville magmatic sulfide deposit was directly detectible with EM methods, and the passive and active-source methods were complementary to one another. The EM data did not directly detect the Turnagain Alaskan-type deposit, but the magnetics data proved to be successful in defining the geologic framework. Implications for future targeting and exploration for economic Ni mineralization are considered.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.084
GPT teacher head0.363
Teacher spread0.280 · 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 designOther design
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

Citations2
Published2023
Admission routes2
Has abstractyes

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