Digital terrain elevation models produced using radar altimetry and GPS data
Bibliographic record
Abstract
Acquisition of any airborne geophysical data set involves two parameters: measurement of the actual signal being sought and definition of the position where each data observation is acquired. GPS provides accurate, estimates of the location of the sensor position relative to a defined ellipsoid. If at the same time one measures the distance from the observation platform to the surface of the Earth, using a radar altimeter, it is then possible to obtain an estimate of the elevation of the Earth's surface at that point. By generating a grid image of discrete elevation data it is possible to produce a digital terrain elevation model (DTEM) of the survey area. Most aeromagnetic surveys comprise a series of flight lines and orthogonal tie-lines. With ideal data a second pass over the same location (either on the tie-line versus the flight-line, or even en a subsequent survey) should give the same elevation. However, attributes of the source data and characteristics of the terrain being modeled can significantly affect the accuracy of results. Comparing elevation data generated from two aeromagnetic surveys of the same area in Southern Alberta shows it is necessary to apply a series of corrections to elevation data just as one might with aeromagnetic data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".