Making Earth System Data Records for Use in Research Environments (MEaSUREs) Greenland Ice Sheet Velocity Map from Interferometric Synthetic Aperture Radar (InSAR) Data, Version 2, 2000-2018
Bibliographic record
Abstract
This data set, part of the National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains seasonal (winter) ice sheet-wide velocity maps for Greenland. The maps are derived from Interferometric Synthetic Aperture Radar (InSAR) data obtained by the Canadian Space Agency's (CSA) RADARSAT-1, the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observation Satellite (ALOS), and the German Aerospace Center's (DLR) TerraSAR-X/TanDEM-X (TSX/TDX) satellites, as well as from the European Space Agency's (ESA) C-band Synthetic Aperture Radar data from Copernicus Sentinel-1A and -1B. This data set contains eleven winter Greenland ice sheet-wide mosaicked velocity maps derived from Synthetic Aperture Radar (SAR) data. Depending on the year, different platforms and sensors were used to produce these data (see Table 3). For each winter, a shapefile is included to indicate the source satellite image pairs that were processed to produce the mosaic. Since speckle tracking may fail to produce results at some points within a SAR image pair, the swaths listed in the shapefile only indicate which data could have contributed to a particular point (i.e., some data from that swath were used in the mosaic, but at any particular point, there may not have been a valid result from that swath). Joughin, I., B. Smith, I. Howat, and T. Scambos. 2015, updated 2018. MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/OC7B04ZM9G6Q. 13 Nov 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.033 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".