MétaCan
Menu
Back to cohort

Spaceborne radar measurements of the eruption of Soufrière Hills Volcano, Montserrat

2002· article· en· W2129222392 on OpenAlexaff
G. Wadge, B. Scheuchl, N. F. Stevens

Bibliographic record

VenueGeological Society London Memoirs · 2002
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyVolcanoSeismology

Abstract

fetched live from OpenAlex

Abstract Radar measurements from space are used to help monitor the evolution of a Peléean eruption on Soufrière Hills Volcano, Montserrat, during 1996 to April 1999. Data from four radar systems are used: ERS-1, ERS-2, Radarsat and JERS-1. We demonstrate that ratio images of backscattered radar energy collected at different times provide useful qualitative summary maps of gross topographic change (e.g. infilling of valleys with deposits) and changes in backscattering properties with time. Radar phase data using the interferometry technique can also provide valuable change detection information. Phase coherence images for ERS-1 and ERS-2 pairs with only one day separation on 25 and 26 September 1997 and 8 and 9 April 1999 allow the areal extent of some pyroclastic flows deposits emplaced during those 24-hour periods to be mapped. Generally, the radar phase can only be retrieved from those parts of the volcano where the vegetation is destroyed by pyroclastic flow deposits and local slopes are not too steep. Quantitative information on the topography of the volcano can also be extracted from the phase data, though not routinely from the new lava dome that grew during 1995-1998. By comparing the radar-measured post-eruption topography with the pre-eruption topography the thickness of the pyroclastic flow deposits (up to 85 m in some valleys on the northeastern slopes) are mapped. Radar interferometry also supplies a means of measuring surface deformation between radar images.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

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

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 head0.205
Teacher spread0.182 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations35
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueGeological Society London MemoirsSame topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207