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Record W4235678604 · doi:10.1190/tle31121428.1

Introduction to this special section: Passive seismic and microseismic—Part 2

2012· article· en· W4235678604 on OpenAlexaff
Julie Shemeta, Bill Goodway, Mark J. Willis, Werner M. Heigl

Bibliographic record

VenueThe Leading Edge · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersU.S. Department of Energy
KeywordsMicroseismInduced seismicitySpecial sectionHydraulic fracturingGeologySeismologySection (typography)Passive seismicGeotechnical engineeringEngineeringEngineering physicsComputer science

Abstract

fetched live from OpenAlex

Welcome to the second half of TLE's two-part special section on passive seismic and microseismic. This month, we focus again on monitoring hydraulic fracturing with microseismic with five articles, but also expand beyond “micro” seismicity, to include unintended “induced” seismicity that may occur during injection. Five articles in this special section focus on induced-seismicity topics. In this introduction, we will highlight various issues related to undesired induced seismicity which may be caused by hydraulic fracturing and deep, underground salt water disposal.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0620.040

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 head0.017
GPT teacher head0.219
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 designNot applicable
Domainnot available
GenreEditorial

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
Published2012
Admission routes1
Has abstractyes

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