Abschaetzung von Steinschlaggefahren an Eisenbahnlinien mittels GIS-Techniken
Bibliographic record
Abstract
Die kanadischen Eisenbahnlinien sind einer grossen Anzahl unterschiedlicher geologischer Naturgefahren ausgesetzt. Die Kontrolle dieses raeumlich stark gestreuten Gefahrenpotenzials erfordert den Einsatz neuartiger Technologien. Ein geographisches Informationssystem (GIS) verfuegt zwar ueber saemtliche Moeglichkeiten, die fuer ein derartiges Risikomanagement erforderlich waeren, wird aber traditionell lediglich als passives System fuer die reine Datenverwaltung verwendet. Fuer die dreidimensionale Modellierung von Steinschlag wurde der RockFall Analyst entwickelt, eine Ergaenzung zu ArcGIS. Ein Gleisabschnitt, in dem in der Vergangenheit Steinschlagaktivitaeten aufgezeichnet wurden, wurde mit dem RockFall Analyst untersucht. Die Resultate der Simulation stimmen sehr gut mit den historischen Aufzeichnungen ueberein. Der RockFall Analyst ist in der Lage, das Gefahrenpotenzial aus Steinschlag aufgrund eines digitalen Hoehenmodells rasch abzuschaetzen. (A) ABSTRACT IN ENGLISH: Canadian railways are exposed to numerous types and magnitudes of natural ground hazards. Managing these spatial hazards requires enabling technology. Geographic Information Systems (GIS) have many of the features required for a hazard management system but are traditionally used as passive system to store data. RockFall Analyst, a three-dimensional extension to ArcGIS, was developed for rock fall process modelling. A section of railway track with historical rock fall records was evaluated using RockFalI Analyst. The results from the rock fall simulation were in agreement with the historical records. RockFall Analyst provides a rapid means of assessing rock fall hazards using digital elevation models. (A)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".