Ground deformation occurring in the Greater Vancouver region, British Columbia, from twenty years of ERS-ENVISAT-RADARSAT-2 InSAR observations
Bibliographic record
Abstract
Ground subsidence in the south-western part of British Columbia, the third largest metropolitan area in Canada with over 2.2 millions of inhabitants, was measured using the Multidimensional Small Baseline Subset (MSBAS) advanced Differential Interferometric Synthetic Aperture Radar (DInSAR). The MSBAS (Samsonov and d'Oreye, 2012) software calculates two dimensional time series of ground deformation from multiple DInSAR data sets acquired with various acquisition parameters (e.g. spatial and temporal resolution and coverage, wavelength, azimuth and incidence angles). The two dimensional time series produced here have improved temporal resolution, almost uninterrupted coverage and lower noise. The Synthetic Aperture Radar (SAR) data used in this study consists of seven independent sets: one ascending and one descending ERS-1/2 and ENVISAT frames, together spanning July 1995 - September 2010, and three RADARSAT-2 frames spanning February 2009 - October 2012. During the July 1995 period October 2012 we observed fast ground subsidence with a maximum rate greater than -2 cm/year in the Greater Vancouver region that includes the Fraser River delta and the cities of Burnbary, Richmond, Surrey, and Vancouver. The fastest subsidence was observed beneath the Vancouver International Airport and around agricultural and industrial regions. Rapid sub-centimeter ground deformation also occurred during the summer and fall of 2009-2012. These time series suggest that the subsidence rate at the studied regions does not decrease with time as suggested in previous studies but actually increases. The long term impact of subsidence on infrastructure can be significant and needs to be investigated further.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".