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Record W3146536897 · doi:10.18280/ijdne.160103

The Utility of Morphometric Parameters Extracted from SAR Radar Images in the Monitoring of the Dynamics of the Danube Island System, Giurgiu-Călăraşi Sector, Romania

2021· article· en· W3146536897 on OpenAlexvenueno aff
Kamel Hachemi, Florina Grécu, Gabriela Ioana‐Toroimac, Ştefania Grigorie-Omrani, André Ozer, Catherine Kuzucuoğlu

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsnot available
FundersEuropean Space Agency
KeywordsTributaryErosionGeographyRadarCurrent (fluid)Physical geographyHydrology (agriculture)GeologyGeomorphologyCartographyOceanography

Abstract

fetched live from OpenAlex

The Danube islands system is continuously undergoing real degradations caused by natural and anthropogenic processes, thus causing a weakening of the biological potential and generating ecological and socio-economic imbalances. The lack of frequent data on the morphometric parameters of the islands in this region constitutes a major gap for monitoring, understanding and diagnosing the state of their evolution. The aim of this work is to show the importance of morphometric parameters extracted from SAR radar amplitude images in the monitoring of the dynamics of the Danube island system, along the Giurgiu-Călăraşi sector, at the frontier of Romania – Bulgaria. This study conducted by extraction of islands allowed us to detect and monitor the evolution of each island and sub-island, between 1995 and 2009, with great precision. The results obtained showed a displacement of the island system in the South-East direction with an average annual velocity of about 1 m/year and sediment accumulations with an average radius velocity estimated at 1.34 m/year during this period of 14 years. The increase of sediment accumulation during the studied period is due to river bank erosion and to the major floods produced in 2005 on the tributary rivers and especially those of 2006 on the Danube River.

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 score0.015
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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