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Record W2561816591 · doi:10.1201/9781315388502-18

Tensor variate normal distribution for stress variability analysis

2016· article· en· W2561816591 on OpenAlexaff
Ke Gao, J. P. Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRandom variateStress (linguistics)Tensor (intrinsic definition)Normal distributionCauchy stress tensorDistribution (mathematics)Computer scienceMathematicsStatisticsGeometryMathematical analysisRandom variable

Abstract

fetched live from OpenAlex

The mines of S’Argentera (from the Catalonian “La Platera”) are a complex of underground argentiferous lead mines that have lain abandoned since 1909. They are located in the proximities of the small picturesque village of San Carles on the Island of Ibiza (Balearic Islands, Spain) in what is now a highly touristic area (Fig. 1). There are documents testifying to the existence of the mines from the XIV century, but the height of their exploitation was between 1867 and 1909, when they were abandoned due to flooding in the richest part of the mine. Strangely, the complex is now completely dry and the ground water level is 20 m lower. Both the external and underground heritage form one of the most important mining relics of the Balearic Islands and the area is in the study stage to turn it into a tourist attraction (Escandell Serra 2001).

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2016
Admission routes1
Has abstractno

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