Fiber interferometric system for vehicle monitoring near railway level crossings
Bibliographic record
Abstract
The article describes the development of the vehicle monitoring system that is applicable in the vicinity of the railway level crossings.The proposed system is based on a Mach-Zehnder fiber interferometer sensor, which has a measurement arm embedded in the road surface.The sensor is passive and requires no electrical power at the installation site, immune electromagnetic interference, and is compatible with existing single-mode fiber infrastructure.Experimental measurements were focused on the response of sensors utilizing different mechanical structures of fiber loops, slow-moving vehicles, and sensor function in continuous snow cover.Installed optical fibers in the roadway are regularly monitored and evaluated for their durability.The results have shown that it is possible to detect individual vehicles but above all their axles, which opens the way for vehicle classification.The detection capability of the system is absolute even with continuous snow cover.For slow-moving vehicles, double axle detection occurred due to the longitudinal dimensions of the loop, which will be solved by the second generation of sensors.The resulting system will be installed on a selected railroad crossing as a complement to the existing camera system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".