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Record W4291754046 · doi:10.1190/image2022-3746215.1

Uranium exploration in the Athabasca Basin using low-noise airborne gravity gradient data

2022· article· en· W4291754046 on OpenAlexaboutno aff
Darren Burrows, James T. Sykes

Bibliographic record

VenueSecond International Meeting for Applied Geoscience & Energy · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyNoise (video)Structural basinUraniumRemote sensingGeodesyEnvironmental scienceComputer scienceGeomorphologyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The Athabasca Basin, northern Saskatchewan, Canada, is renowned for the largest, high-grade uranium deposits in the world. Historically, most of the deposits have been discovered within the Basin with as much as 700 metres of overlying sandstone cover. A new generation of shallow, basement-hosted deposits are currently being explored for near the margins and outside of the Basin. We will show examples of low-noise airborne gravity gradient data, and how it is sensitive to zones of hydrothermal alteration which could be indicative of uranium fertile fluid systems. The gravity gradient data was combined with structural interpretations of airborne magnetic and EM data to highlight prospective drill targets.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.029
GPT teacher head0.247
Teacher spread0.218 · 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 designSimulation or modeling
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

Explore more

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