Development and Validation of a Railway Safety System for Nordic Trains in Isolated Territories of Northern Quebec Based on IEEE 802.15.4 Protocol
Bibliographic record
Abstract
Connected objects are deployed all over the world. Thus, they are contributing to improving communications. In urban areas, technological challenges are gradually being overcome, and advances in this area are exponential. Unfortunately, isolated territories such as northern Quebec do not beneficiate from this technological progress. Yet, northern Quebec relies on abundant natural resources, with notably its huge hydroelectric dams and iron mines, and therefore, the region's economic life revolves essentially around the exploitation of these resources and is heavily reliant on rail transportation. However, according to Transport Canada, 1246 railroad accidents were reported in 2019 to the Transportation Safety Board (TSB). Thirty-eight people described as trespassers lost their lives, and five railroad employees were fatally injured. In this context, we present the implementation of a security system in an isolated environment for employees intervening on the railroad track to warn them of the imminent arrival of a train. Due to the context of the isolated environment, i.e., without an electrical network, without internet, and without an LTE network, a solution for employees has been developed using a Zigbee telecommunication system and a connected watch. A case study on a train operating in a remote and isolated area in northern Quebec is presented to validate the performance of the proposed system based on an open-source and customizable solution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".