Geohazard risk management for linear transportation
Bibliographic record
Abstract
Railways that deliver ore from mines to market are critical to an operation’s viability. Two examples of such railways include the Rio Tinto Iron Ore (RTIO) railway in the Pilbara region of Western Australia, and the Iron Ore Company of Canada (IOC) railway in northeastern Canada. Both railways are the only transportation mode from 17 mine sites to the ports to deliver their products to markets; annually, these railways ship over 330 million tonnes of iron ore (RTIO) and over 10 million tonnes of iron pellets (IOC). Although separated by over 16,000 km, different terrains, climates and operating regulations, these railways face similar challenges with respect to assessing and managing the risks associated with geohazards, in particular rockfall, landslides, and flooding geohazards. This paper presents risk-based frameworks for the IOC and RTIO railways, and the development of web and mobile based platforms to support effective geohazard risk-management practices within corporate risk frameworks. The output risk rating for each credible geohazard affecting the railway is used to support risk management through inspections, remediation projects and optimisation of maintenance and in situ or remote monitoring efforts. The geohazard management systems are also used in combination with live monitoring data to actively alert railway operators of changing conditions and potential triggering events, such as flooding or heavy rainfall. The systems that will be presented are used to support decisionmaking and communication of geohazard threats within their organisations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".