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Record W6999967115

The distance analysis of a mine scale event

2022· dissertation· en· W6999967115 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsInduced seismicityEvent (particle physics)Scale (ratio)Dispersion (optics)Magnitude (astronomy)Fault (geology)Spatial ecology
DOInot available

Abstract

fetched live from OpenAlex

Mining induced seismic events greater then Nuttli Magnitude 3.0 are difficult to understand, have \nhigh potential consequences and are becoming increasingly common in Canada. The term mine \nscale event (MSE) is used to describe a seismic event in which the mechanisms and processes \ninvolved take place on a scale similar to that of the mine. \nA MSE from Nickel Rim South Mine was investigated using seismic data to explain its time, \nlocation and large magnitude. A novel tool, Time Distance Analysis was developed to identify \nspatial-temporal trends in seismicity around the MSE. Guidelines were developed to account for \nthe unknown spatial and temporal extent of the processes that led to and were affected by the \nMSE. \nThe results showed that preceding seismicity tended to coalesce around the eventual hypocenter \nof the MSE while subsequent seismicity migrated away. The coalescence was interpreted to \nrepresent the deterioration of a fault asperity, leading to an eventual rupture. After the MSE \noccurred, the dispersion of seismicity was interpreted to represent an unloading of the source \nregion.

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)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.194
Teacher spread0.187 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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