Synthetic aperture radar for coastal erosion mapping and land-use assessment in the moist tropics: Bragança coastal plain case study
Bibliographic record
Abstract
With SAR's side viewing geometry, longer wavelengths, and almost all-weather sensing capability, RADARSAT-1 imagery has been extensively used as monitoring tool for coastal changes in the moist tropics. In this investigation, RADARSAT Fine Mode data acquired in 1998 was combined with airborne SAR X-HH GEMS acquired in 1972 during the RADAM Project and it was possible to evaluate the large-scale coastal changes occurring over the past three decades.The orbital SAR data was digitally geometric corrected (ortho-rectified)and filtered for speckle noise. The airborne SAR data, originally available on mosaic format, was scanned and geometrically corrected through polynomial method. A simple method to estimate shoreline changes was carried out based on the superimposition of shoreline vectors extracted from the airborne radar and related features present on the RADARSAT data. The results of the investigation have allowed characterizing changes in the area associated with shoreline retreat and accretion. In addition, the estuarine and tidal channel displacements have also provided an understanding of the coastal sedimentary dynamic, marine transgression and sea-level changes in this sector of the Northern Brazilian coast.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".