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Record W2791589557

Earth Observation-based Monitoring of Volcanoes - the contribution of the International Charter 'Space and Major Disasters'

2018· article· en· W2791589557 on OpenAlexaboutno aff
Simon Plank, Sandro Martinis

Bibliographic record

Venueelib (German Aerospace Center) · 2018
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsCharterVolcanoContext (archaeology)GeographyRemote sensingGermanPolitical scienceMeteorologyGeologySeismologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The International Charter 'Space and Major Disasters' is an international consortium of space agencies and satellite operators that aims at providing a unified system of space data acquisition and delivery to those affected by natural or man-made disasters. The Charter was founded by the European, the French and the Canadian space agencies (ESA, CNES and CSA) in the year 2000. Following countries subsequently joined the Charter: the USA, India, Argentina, Japan, UK, China, Germany, Korea, Brazil, Russia, Bolivia and the Arab Emirates. Also the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is a Charter member. This article presents the activities of the Charter in the context of Earth Observation-based disaster monitoring of large volcanic eruptions. In the last 18 years the Charter has been activated 34 times due to larger volcanic events, starting with the eruption of Mount Etna, Sicily, Italy in 2001 to the currently on-going eruptions on Papua New Guinea's Kadovar Island and of Philippines' most active volcano, Mount Mayon. Since 2010 the German Aerospace Center (DLR) is member of the Charter and contributes RADAR imagery of the TerraSAR-X mission, RapidEye optical imagery as well as Value Adding, i.e. the extraction of relevant crisis information from satellite data and the transfer of this information into geo-information products, such as maps. Focus of this article is the 16 volcano-related Charter activations where DLR was actively involved.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.251

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 science0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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