MétaCan
Menu
Back to cohort
Record W4322007623 · doi:10.5194/egusphere-egu23-8364

Interferometric Synthetic Aperture Radar (InSAR) mapping in Vlaykov Vruh and Tsar Assen Cu-porphyry deposits, Panagyurishte ore region, Bulgaria

2023· preprint· en· W4322007623 on OpenAlexaboutno aff
Guillem Domènech, Kamen Bogdanov, Daniel Nieto-Yll, Azadeh Faridi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInterferometric synthetic aperture radarGeologyDisplacement (psychology)InterferometryTerrainSeismologySynthetic aperture radarSatelliteGeodesyRemote sensingGeographyCartographyOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Interferometric Synthetic Aperture Radar (InSAR) has been applied using SAR images from the European Space Agency (ESA) Sentinel-1 constellation, in descending orbit, to obtain the terrain displacement by means of the Coherent Pixel Technique (CPT). This Persistent Scatter Interferometry (PSI) technique was developed in 2002 by the Remote Sensing Laboratory (RSLab) of the Universitat Politècnica de Catalunya, UPC (Lanari et al., 2004; Mora et al. 2002), and recently updated by the Dares Technology team.ESA Sentinel-1 satellite constellation images were used with Single Look Complex (SLC) images and Interferometric Wide Swath (IW) acquisition mode to detect terrain displacements at Vlaykov Vruh and Tsar Assen porphyry-copper deposits (PCD) in the southern part of Panagyurishte ore district in Bulgaria.The detected displacement magnitude in Vlaykov Vruh was from 500 to 4,000 m2 while for Tsar Assen PCD it ranges from 500 to 2,500 m2 where several spots of displacement were detected.We conclude that in the waste pile area east of the Vlaykov Vruh slope instabilities occurred with a displacement of 3.5 cm. Due to a landslide along the fault structure, a slope displacement of about 4.0 cm for Tsar Assen PCD was detected.The study is supported by the Horizon 2020 co-funded GOLDENEYE project, which has received funds through Grant Agreement 869398. References:Lanari, R.; Mora, O.; Manunta, M.; Mallorqui, J.J.; Berardino, P.; Sansosti, E. 2004. A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms.’ IEEE Trans. Geosci. Remote Sens., 42, 1377–1386.Mora, O.; Mallorqui, J.J.; Duro, J. 2002.Generation of deformation maps at low resolution using differential interferometric SAR data.’ Proceedings of 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’02, Toronto, ON, Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.226
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207