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Record W3124914662 · doi:10.1038/s41598-021-82292-3

Detection of volcanic unrest onset in La Palma, Canary Islands, evolution and implications

2021· article· en· W3124914662 on OpenAlexaff
José Fernández, Joaquín Escayo, Zhongbo Hu, Antonio G. Camacho, Sergey Samsonov, Juan F. Prieto, K. F. Tiampo, Mimmo Palano, Jordi J. Mallorquí, Eumenio Ancochea Soto

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNatural Resources Canada
FundersMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaConsejo Superior de Investigaciones CientíficasMinisterio de Ciencia e InnovaciónMinisterio de Ciencia, Innovación y Universidades
KeywordsVolcanoUnrestArchipelagoGeologySeismologyVolcanic hazardsSubmarine volcanoGeodetic datumOceanographyGeodesy

Abstract

fetched live from OpenAlex

La Palma island is one of the highest potential risks in the volcanic archipelago of the Canaries and therefore it is important to carry out an in-depth study to define its state of unrest. This has been accomplished through the use of satellite radar observations and an original state-of-the-art interpretation technique. Here we show the detection of the onset of volcanic unrest on La Palma island, most likely decades before a potential eruption. We study its current evolution seeing the spatial and temporal changing nature of activity at this potentially dangerous volcano at unprecedented spatial resolutions and long time scales, providing insights into the dynamic nature of the associated volcanic hazard. The geodetic techniques employed here allow tracking of the fluid migration induced by magma injection at depth and identifying the existence of dislocation sources below Cumbre Vieja volcano which could be associated with a future flank failure. Therefore they should continue being monitored using these and other techniques. The results have implications for the monitoring of steep-sided volcanoes at oceanic islands.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.217
Teacher spread0.206 · 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 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

Citations60
Published2021
Admission routes1
Has abstractyes

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