Filtering Global Land and Surface Altimetry Data (GLA14) for Elevation Accuracy Determination
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents a filtering method for ices at Global Land Surface Altimetry data (GLA14), which is based on indicators to detect potentially contaminated GLA14 elevation points. Potential contamination sources include attitude miscalculation, saturated echoes, equipment noise, the atmosphere, and variable elevation within footprints. For a study site located in Northern Canada, this multi-indicator filter provided a 19 percent reduction in the root mean square error for elevation, when compared to Canadian Digital Elevation Data (CDED). This result dem onstrates the method’s ability to provide an improved dataset for vertical accuracy evaluation, with respect to unfiltered GLA14 data. The improvement was achieved with a rejection rate of 69 percent. However, due to the high density of the unfiltered GLA14 data over the study site, a spatially homogeneous distribution of elevation points was maintained, even after filtering. Results also showed the rejection efficiency of most indicators, as well as their complementarity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it