Digital Elevation Model of coastal and flood plains obtained from vector data: an alternative method applied to the Coatzacoalcos fluvial plain (Mexico)
Bibliographic record
Abstract
Vector data can be used to generate Digital Elevation Models (DEMs), but in the case of fluvial and coastal plains, according to topographical constrains, the interpolation requires other types of intervention to achieve a result of quality. For Mexico, neither data base check points nor LiDAR Digital Terrain Models (DTMs) are available for such areas, so that vector data are frequently used to generate DEMs. This lack of information led us to propose a method to obtain an alternative DEM by using the available vector data, especially because these fluvial and coastal plains are frequently affected by floods. The RMSE (root mean square error) as well as the standard deviation of the generated DEM are lower (0.0074 and 0.0059) than for the LiDAR DTM (0.0945 and 0.0793). On the other hand, the validation is also based on the comparison of flood simulation applied to the resulting DEM and the corresponding LiDAR DTM. The area covered by the simulated flood is 716.63 km2 for the resulting DEM; meanwhile, an area of 787.82 km2 is obtained using LiDAR DTM. Concerning the volume, this is 1.02 km3 for the DEM and 0.86 km3 for the LiDAR DTM.
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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) | 0.002 | 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".