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Record W2054115274 · doi:10.1109/igarss.2015.7326564

Distribution of glacial and periglacial features within ice-free areas surrounding Maxwell Bay (South Shetland Islands) using polarimetric RADARSAT-2 data

2015· article· en· W2054115274 on OpenAlexfundno aff
Thomas Schmid, Jerónimo López-Martı́nez, Stéphane Guillaso, Olivier D’Hondt, Magaly Koch, Sandra Mink, A. Nieto, Enrique Serrano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsShetlandGeologyPermafrostLandformGlacial landformGlacial periodGeomorphologyRemote sensingPhysical geographyBayOceanographyGeographyMoraine

Abstract

fetched live from OpenAlex

Active periglacial processes and landforms are common within the Northern Antarctic Peninsula region and are becoming the focus of interest for studying changes occurring to permafrost. The objective of this study was to identify and characterize glacial and periglacial surface features with fully polarimetric SAR C band RADARSAT-2 data in ice-free regions surrounding Maxwell Bay, South Shetland Islands. Extraction of polarimetric parameters, a selection of field based training sites and a supervised classification approach, were chosen to determine the spatial distribution of the different geomorphological surface features. These results show the identification of complex and relatively small scale geomorphological features such as periglacial landforms that are an important indicator of the presence of permafrost.

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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.274
Teacher spread0.183 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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