USING GPS FOR AUGMENTING DEFORMATION MONITORING SYSTEMS IN OPEN PIT MINES—PROBLEMS AND SOLUTIONS
Bibliographic record
Abstract
Les grandes mines a ciel ouvert exigent une surveillance continuelle de la stabilite des parois des gradins. Dans la plupart des cas, une exactitude de l om ou mieux a un niveau de confiance de 95 % est necessaire dans la detection du deplacement de centaines de cibles. A l'heure actuelle, les stations totales robotisees (STR) fournissant une reconnaissance automatique des cibles s'averent la solution la plus efficiente pour le probleme de surveillance. Dans les grandes mines a ciel ouvert, il pourrait etre necessaire de placer les STR pres du fond de la mine dans des conditions instables sans visibilite vers des points de reference stables. Des essais approfondis ont ete effectues dans une grande mine a ciel ouvert pour evaluer l'utilisation des GPS pour controler la stabilite des STR. Le but etait d'obtenir des corrections GPS pour la position des STR avec un ecart-type infericur ou egal a 2,5 mm pour chacune des trois composantes (N, E, h). Cinq jours de donnees continuelles de GPS a differents niveaux de la mine a ciel ouvert ont indique que les principales limites pour respecter les exigences en matiere d'exactitude sont des delais tropospheriques residuels et une visibilite limitee des satellites. Pour ameliorer le rendement du systeme combine de surveillance STR/GPS, plusieurs solutions de remplacement ont ete suggerees, y compris l'ajout de pseudolites aux GPS et l'utilisation d'une technique de filtrage adaptative dans le traitement des donnees GPS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".