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Record W7035920311

Application of Sentinel-1 data to quantify Arctic Coastal Retreat

2021· dissertation· en· W7035920311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2021
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsErosionRadarSynthetic aperture radarArticular cartilage damageRadar trackerFront (military)
DOInot available

Abstract

fetched live from OpenAlex

In the arctic region many coastal areas exhibit rapid erosion, with coastal retreat or erosion rates of 10 meters per year (m/yr.) or higher in places. This poses a threat primarily to all manner of infrastructure built directly on and near the coastline. With climate change the coastal erosion is expected to increase. This effect is expected to be especially severe in the continuous permafrost region, as the coastal erosion is linked with the increase in thermoerosion of the permafrost. A remote sensing method with high coverage and sufficient temporal observation frequency at lower cost than from aerial photography would be practical to mitigate the problem. This would enable assessing and predicting (potential) damage to existing infrastructure and planning of its future locations. A thresholding method based on TerraSAR-X x-band synthetic aperture radar observations is applied to lower resolution Sentinel-1 c-band synthetic aperture radar observations monitor the coastal erosion rates. This study aims to determine the feasibility of using this method with Sentinel- 1 data. For this purpose, the method is applied to Senteinel-1 Backscatter scenes and the Coherence between scenes within each year from 2016 to 2020 at three sample sites on Herschel Island (Beaufort Sea, northern Canada). The method was successfully applied to the Sentinel-1 Backscatter data, yielding reliable and accurate results for one of the sample sites, with the highest estimated erosion rate of the three sites. The same technique was applied to the Coherence data. The obtained results were less reliable compared to the results from the Backscatter data, showing too high variance. The results indicate that the application is generally limited to the summer season and to coastlines oriented towards or parallel to the looking direction of the SAR sensor. The results are compared to previous studies of coastal erosion rates on Hershel Island or the nearby northern Yukon coastline region.

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.

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 score0.117
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.282
Teacher spread0.244 · 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