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Record W2514682430 · doi:10.3997/2214-4609.201602093

Analysis and Visualization of Dynamic Elastic Properties in 3D Space for High Density Minerals

2016· article· en· W2514682430 on OpenAlexaff
A. Kassam, B. Milkereit, Daxin Shi

Bibliographic record

VenueProceedings · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVisualizationCharacterization (materials science)Elastic modulusPlane (geometry)Computer scienceSpace (punctuation)GeologyMaterials scienceMineralogyData miningComposite materialMathematicsNanotechnologyGeometry

Abstract

fetched live from OpenAlex

Summary Conventional empirical relationships generally do not hold for high-density ores. Seismic parameter, density, and bulk modulus plotted in a 3D space creates a new framework for visualizing and interpreting datasets. The advantage of using a three-parameter configuration is the data from all types of rock materials falls onto a plane. This new framework can be used for trend analysis of various materials such as common silicates, iron-oxides, and massive sulphides and provide a link for extracting dynamic elastic parameters and aid in rock characterization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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