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Record W4394035246 · doi:10.5281/zenodo.3900656

Microseismic Catalog for ToC2ME dataset

2020· dataset· en· W4394035246 on OpenAlexaffabout
Nadine Igonin

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicroseismGeologyGeographyComputer scienceCartographySeismology

Abstract

fetched live from OpenAlex

Microseismic catalog for the Tony Creek dual Microseismic Experiment (ToC2ME) dataset, a passive microseismic dataset collected near Fox Creek, Alberta, Canada, in 2016. Number of events: 18040 Location/detection method: Absolute locations based on P- and S-waveforms from 3C sensor. Method involves stacking waveforms along ray-tracing travel times within a volume. Includes all events that exceeded quality control criteria from a larger catalog of detected events from multi-channel 3D cross-correlation. High resolution in x and y, but with a large uncertainty in depth. Method developed by James Verdon at the University of Bristol. Magnitudes obtained from 3C geophones using the standard Brune formula. See https://github.com/ToC2ME for more information.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0040.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.027

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 head0.043
GPT teacher head0.259
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes2
Has abstractyes

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