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Yamarna Geology: Foundations for Further Discovery

2019· article· en· W2984823678 on OpenAlexaff
Janet Tunjic, John C. Donaldson, Justin Osborne, Clayton Davy’s, R. -A. Berg

Bibliographic record

VenueASEG Extended Abstracts · 2019
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsTerraneYilgarn CratonGeologyGeochronologyPaleontologyEarth scienceCratonTectonics

Abstract

fetched live from OpenAlex

SummaryWorld class gold discoveries are being made in the Yamarna Terrane, one of the least understood terranes of the Yilgarn Craton. Despite many and varied challenges, Gold Road are in the unique position of compiling and constructing the stratigraphic map for this immature exploration terrane allowing continual improvement to ongoing targeting and exploration programmes.The key to successful exploration is the development of a regional stratigraphic and structural geological model through acquisition of new data, application of modern science, and integration of fundamental geological observation. Lithogeochemical classifications, geochronology, and development of mineralisation models are the three areas of focus Gold Road considers paramount to combine with the fundamental structure of the Yamarna Terrane. New geochronological data has implications for the understanding of the geodynamic evolution of the Yilgarn Craton, identifying some of the oldest rocks so far found in Eastern Goldfields Superterrane.A case study is presented of the development in the understanding of the tectonostratigraphic evolution of the Yamarna Terrane to target for gold efficiently and effectively.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.006
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.002

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 head0.250
Teacher spread0.236 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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