Comparing TIN random densification with the mean profile filter to minimize the ridging phenomenon in Service New Brunswick digital terrain models
Bibliographic record
Abstract
Cet article presente les resultats d'une recherche sur la conception d'une approche pratique et eclairee permettant d'eliminer l'effet de « crete » ou « l'effet » « pyjama » dans environ 1890 modeles numeriques distincts de terrain au Nouveau-Brunswick, Canada. Le processus et les resultats de l'approche - TIN Random Densification (densification aleatoire TIN) -- sont decrits en detail et compares brievement a la methode « Mean Profile Filter (MPF) (filtre de profil moyen) » de la Geological Survey des E.-U. La methode MPF a ete concue pour atteindre le meme objectif que la densification aleatoire TIN. Les resultats des tests indiquent que l'approche de la densification aleatoire TIN a pu reduire de facon considerable les effets de crete sans nuire a la precision specifiee des fichiers des MNT. L'approche MPF a moins bien reussi, mais cela pourrait s'expliquer par la structure intrinseque des fichiers des MNT des Services Nouveau-Brunswick. Le processus de densification aleatoire TIN sera mis en œuvre dans l'ensemble de la province a l'aide de contrats a l'industrie par l'entremise des Services Nouveau-Brunswick, au cours de la periode 2000-2001.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".