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Record W2157218486 · doi:10.1071/aseg2013ab283

New constraints on an existing mineral resource through 3D seismic

2013· article· en· W2157218486 on OpenAlexaff
Chris Wijns, Alireza Malehmir, Emilia Koivisto

Bibliographic record

VenueASEG Extended Abstracts · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsFirst Quantum Minerals (Canada)
Fundersnot available
KeywordsMaficGeologyLayeringGeochemistryHorizonIntrusionMineral resource classificationLayered intrusionPetrologyGeometry

Abstract

fetched live from OpenAlex

The Kevitsa nickel-copper deposit in northern Finland is a large, low-grade, mafic-hosted accumulation of disseminated sulphides with rare, spatially restricted occurrences of net-textured to semi-massive sulphides. The mineable limits of the resource grow or shrink with commodity prices, but it has also been recognised that subtle mafic layering in the intrusion controls sub- horizontal layering of sulphides. The net-textured and semi-massive mineralisation styles occur near the base of the intrusion. Data from a 3D seismic survey demonstrate the unpredicted ability to image the sub-horizontal mafic layering, as well as the expected reflections at the base of the intrusion in contact with interlayered volcanic and sedimentary country rocks. The ability to trace the lateral extents of the mafic layering, backed up by analysis of borehole sonic and density logging, offers the possibility to predict the ultimate envelope of the resource. The interpretation of the base of the intrusion provides a horizon along which to target the net-textured to semi- massive contact mineralisation.

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)0.002
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.254
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

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
Published2013
Admission routes1
Has abstractyes

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