DEM Generation and Data Quality Assessment for Glacial Topography
Bibliographic record
Abstract
L'etude sur l'environnement et les activites decisionnelles connexes necessitent des modeles altimetriques numeriques (MAN) et d'autres produits numeriques exacts et d'une grande fidelite. Cet article evalue la valeur de la production automatique de MAN a l'appui de la visualisation du terrain durant la modelisation de l'evaluation du paysage glaciaire pour la Chaine de montagnes du sud de la Tasmanie en Australie. Deux approches de production de MAN, une a partir d'images et une a partir de courbes de niveau, sont utilisees. L'approche adoptee consiste a comparer des MAN tires d'images et tires de courbes de niveau pour evaluer la relation entre les configurations d'erreurs et les caracteristiques du terrain. La qualite de l'ortho-image pour la visualisation photorealiste est egalement abordee d'apres des methodes empiriques et analytiques. Les difficultes de ce procede sont l'appariement des images de faible contraste et l'interpolation pour la production de MAN pour un terrain complexe. Ainsi, pour un ensemble particulier de donnees, on obtlent de l'experience en choisissant l'approche photogrammetrique et cartographique la plus appropriee pour optimiser l'exactitude et la fidelite de la modelisation d'un terrain de cirques glaciaires.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".