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Record W2275741740 · doi:10.1002/2015gl067418

Rapid automated W‐phase slip inversion for the Illapel great earthquake (2015, <i>M</i><i><sub>w</sub></i> = 8.3)

2016· article· en· W2275741740 on OpenAlexaff
Roberto Benavente, Phil R. Cummins, Jan Dettmer

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Victoria
FundersComisión Nacional de Investigación Científica y TecnológicaAustralian Research Council
KeywordsSeismologyGeologyTrenchInversion (geology)Slip (aerodynamics)RakeGeodesyTectonicsPhysicsGeomorphology

Abstract

fetched live from OpenAlex

Abstract We perform rapid W‐phase finite fault inversion for the 2015 Illapel great earthquake (Mw = 8.3). To evaluate the performance of the inversion in a near real time context, we divide seismic stations into four groups. The groups consider stations up to epicentral distances of 30°, 50°, 75°, and 90°, respectively. The results for the first group could have been available within 25 min after the origin time and the results for the last group within 1 h. The four results consistently show a peak slip of ∼10 m near the trench with trench perpendicular rake which is consistent with the tsunami genesis of the event. The slip location is similar to that in the preliminary U.S. Geological Survey solution. The inversion is automated and provides meaningful results within 25 min after the event. This makes the method particularly suited to emergency management and early warning at regional and teletsunami distances.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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