Laboratoire canadien de recherche ferroviaire : résumé des résultats des plans de recherche 2022-2023
Bibliographic record
Abstract
This report provides the summary of results for the research projects carried out by the Canadian Rail Research Laboratory (CaRRL) for fiscal year 2022-2023. The projects include: • Defect Simulation on Railcar Components for AMVIS: Fake defects were simulated on railcar components and Automated Machine Vision Inspection System (AMVIS) was used to investigate the defects. • Detailed Quantitative Model for Rail Transport of Dangerous Goods: This project developed a detailed, practicable model to quantitatively estimate the risk for rail transport of DG. Fault Tree Analyses (FTA) and Event Tree Analyses (ETA) process model were developed for rail transport operations. • Ground Hazard Risk Evolution with Climate Change: This project evaluated the ground hazard weather triggers and mechanisms through morphological and probabilistic approaches to quantify their relationship with weather and extrapolated the ground hazard frequency utilizing the climatic projections for Canada. • Wildfire Hazard Identification and Risk Assessment: This project aims to develop an automated framework to assess the wildfire-related risks to railway infrastructure. • Development of a Workload Prediction Model for Train Operators: This project aims to develop a workload measurement and prediction model that can estimate train operators' mental workload.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.038 |
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".