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Record W6940247338 · doi:10.7914/g2h0-tn69

Endeavour Extend

2025· dataset· en· W6940247338 on OpenAlexaffabout

Bibliographic record

VenueNSF Seismological Facility for the Advancement of Geoscience (SAGE) · 2025
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of VictoriaOcean Networks Canada SocietyNatural Resources CanadaDalhousie University
Fundersnot available
KeywordsDikeMid-ocean ridgeSubmarinePlate tectonicsOceanic crustVolcanoMarine geologyRidgeCrust

Abstract

fetched live from OpenAlex

This project is a collaborative effort involving scientists from the University of Washington, Dalhousie University, the Geological Survey of Canada, Ocean Networks Canada and the University of Victoria, funded by awards from the NSF, NSERC Ship Time Fund, and MEOPAR. It seeks to take advantage of a rare opportunity for a preemptive enhanced seismic response to a diking spreading sequence that is anticipated on the Endeavour segment of the Juan de Fuca Ridge (JdFR) based on observations of escalating seismicity. Such a sequence will likely occur only once in the 25-year design lifetime of the Ocean Networks Canada (ONC) NEPTUNE observatory. Except for Axial Seamount, which is a hotspot volcano with two rifts and not a ridge segment with enhanced magma supply at the center, there is nowhere else on the 80,000-km-long global network of oceanic spreading centers with the cabled infrastructure in place to anticipate such events and make the multidisciplinary observations necessary to link a detailed geophysical understanding of diking to its impact on vigorous hydrothermal systems and biological communities. Dike injection is the fundamental process that creates the upper oceanic crust and, except at the slowest spreading rates, accounts for the bulk of plate boundary extension. Submarine diking along mid-ocean ridges is most easily detected using hydroacoustic observations of swarm seismicity. In previous studies, hydroacoustic datasets have tracked the propagation speeds of dikes, identified the locations of likely eruptions, and facilitated rapid response efforts to observe their impacts on the seafloor. Yet, much more can be learned about the deformation associated with dike injection if local seismic networks are in place to record the high levels of precursory seismicity when the crust is critically stressed and the rupturing that accompanies dike injection. Beyond volcanic eruption monitoring, this dataset provides valuable submarine seismic data that offer a unique opportunity to investigate marine mammals, including endangered blue whale, by analyzing their behaviours and migration patterns. Seismicity monitoring will also contribute to understanding of major geohazards on the Pacific Coast, with results incorporated in seismic hazard and tsunami prediction models used to protect public safety and coastal infrastructure. A network of 20 Aquarius broadband ocean-bottom seismometers (OBS) from the National Facility for Seismological Investigations (Dalhousie University) will be deployed in August 2025 for a period of approximately 1 year to supplement the existing cabled NEPTUNE observatory during the time period when the Endeavour segment is most likely to rupture. A dense cluster of 16 OBS will be deployed near the center of the segment to cover the footprint of previously observed seismicity, with 2 OBS at either end to fully characterize off-axis seismicity. Continuing deployments of 5 OBS (3 central and 2 at southern end) per year are planned for 2026-2029.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
Teacher disagreement score0.562
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0040.005
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4380.234

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 head0.023
GPT teacher head0.267
Teacher spread0.245 · 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; the direct Gemma label and the distilled Codex classifier 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
Published2025
Admission routes2
Has abstractyes

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