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Record W2300664140 · doi:10.1017/jog.2016.10

A SAR record of early 21st century change in Greenland

2016· article· en· W2300664140 on OpenAlexfundno aff
Ian Joughin, Ben Smith, Ian M. Howat, Twila Moon, T. A. Scambos

Bibliographic record

VenueJournal of Glaciology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersCanadian Space AgencyJapan Aerospace Exploration AgencyNational Aeronautics and Space Administration
KeywordsGeologySynthetic aperture radarGlacierRemote sensingBackscatter (email)GeodesyPhysical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT Glaciers in Greenland are changing rapidly. To better understand these changes, we have produced a series of seven synthetic aperture radar (SAR) backscatter mosaics for seven winters during the period from 2000 to 2013. Six of the mosaics were created using RADARSAT Fine-Beam data and the seventh used ALOS-PALSAR Fine-Beam Single-Polarization data. The RADARSAT mosaics are radiometrically calibrated and capture changes in the backscatter coefficient related to melt and other events, particularly the strong melting in the summer of 2012. Comparison of features in the ascending-orbit ALOS mosaic and the descending-orbit RADARSAT mosaics indicate that in areas of smooth to moderate topography their locations are consistent to within a few tens of meters. The locations of features identifiable in the RADARSAT mosaics, which were collected with the same imaging parameters, generally agree to within better than the 20 m posting of the data. With such geometric accuracy, these data establish a record of change in Greenland for the early part of the 21st century, thus providing a baseline that can be compared with new radar and optical datasets.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.836

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 science0.0000.000
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 head0.039
GPT teacher head0.245
Teacher spread0.206 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

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