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Record W2136653723 · doi:10.1071/aseg2015ab117

Building effective mineral system models; the importance of merging geophysical observation with geological inference

2015· article· en· W2136653723 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueASEG Extended Abstracts · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCondor Petroleum (Canada)
Fundersnot available
KeywordsInferenceComponent (thermodynamics)GeologyEarth scienceComputer scienceGeophysicsData scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The minerals system approach is based on concepts that have been largely germinating for the past 20 years but are now becoming a major component of efforts to rejuvenate minerals exploration into the next decade. Geophysical mapping will take on a far more critical role in this effort as it offers very often the most cost effective means to explore the subsurface, especially when the overlying rocks are unrelated to the targets at depth.This review will examine what might be called the ‘state-of-play’ in how geophysics is currently being used to start to build an understanding of what mineral systems look like in the real world. While much remains to be done, there is encouragement in what has been achieved to date, although most of it typically without the benefit that the overarching concept of developing a mineral system model.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.388

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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