Using emerging technologies for monitoring surface water near railway tracks
Bibliographic record
Abstract
The level of water near the railway track is a major factor affecting the safety of train passage. Prolonged periods of heavy rainfall, rapid snowmelt, flash flooding, river flooding, beaver dams or blockage of a culvert result in a rise of water levels. This situation has been the major cause of many derailments in Canada and resulted in fatalities and serious injuries, damage to environment, loss of property, and service disruption. Railway companies strive to identify the development of problematic water levels in the area surrounding the track. This includes visual inspection performed by qualified track inspectors to visually identify waterway blockage and levels issues and air reconnaissance patrols that take place once or twice each year. These inspections rely on the inspectors’ judgment and experience regarding the water level, have a limited range of coverage, and do not provide visibility on the water issue in the areas that are out of the vision range but still close enough to affect the track. The air reconnaissance patrol covers a larger area and provides a bird’s eye view of all the waterways and identifies blockage of waterway but they are not as frequent. Recent advances in satellite-based remote sensors and tremendous development in unmanned aerial vehicle (UAV) have promoted the field of sensing surface water to a new era. National Research Council Canada and Transport Canada undertook a collaborative research project to evaluate the feasibility of using satellite imagery (including synthetic aperture radar and optical images) and UAV-based RGB images to detect water near railway tracks using data from two test sites in Canada. In addition, Transportation Safety Board (TSB) Rail Occurrence database and TSB’s investigation reports were analyzed to identify the root causes of water-related derailment within Canada’s rail network in the last few decades. 4 Canadian railway operators were also interviewed to better understand their main water related issues. The results of this project suggested that even though these technologies cannot entirely replace the current methods of water inspection, they offer an additional and inexpensive method to provide trackside water information to track inspectors. It was also indicated that further investigations and testing of technologies over same section of track would be required for drawing a definitive conclusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".