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Record W3205056378 · doi:10.4995/cigeo2021.2021.12746

DETECCIÓN DEL INICIO DE REACTIVACIÓN VOLCÁNICA EN LA ISLA DE LA PALMA, ISLAS CANARIAS, Y ESTUDIO DE SU EVOLUCIÓN TEMPORAL

2021· article· es· W3205056378 on OpenAlexaff
José Fernández, Joaquín Escayo, Zhongbo Hu, Antonio G. Camacho, Sergey Samsonov, Juan F. Prieto, K. F. Tiampo, Mimmo Palano, Jordi J. Mallorquí

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEarth and Planetary Sciences
TopicGeological and Tectonic Studies in Latin America
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsHumanitiesTelmatologyGeologyMetamorphic petrologyGeographyCartographyPhilosophySeismology

Abstract

fetched live from OpenAlex

La isla de La Palma es de las de mayor riesgo potencial del archipiélago canario, siendo por tanto importante realizar unestudio en profundidad que permita definir su estado de actividad volcánica. Esto se ha realizado usando observacionesde radar de satélite y una técnica de interpretación original de última generación. Ambas cosas han permitido detectar elinicio de la reactivación volcánica en la isla de La Palma, probablemente décadas antes de una posible erupción. Suevolución temporal muestra una naturaleza cambiante de la deformación volcánica, tanto espacial como temporalmente.El uso de imágenes radar permite obtener resultados con una gran resolución espacial y en un periodo de tiempoprolongado (2009-2020), obteniendo información sobre la naturaleza dinámica de los procesos asociados. Las técnicasgeodésicas empleadas permiten detectar la migración de fluidos inducida por la inyección de magma en profundidad eidentificar la existencia de fuentes de dislocación bajo el volcán Cumbre Vieja que podrían estar asociadas con futurosdeslizamientos, siendo por tanto necesario continuar con la monitorización de este proceso de reactivación utilizandoestas y otras técnicas.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.000

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same topicGeological and Tectonic Studies in Latin AmericaFrench-language works237,207