Bibliographic record
Abstract
Winter provides an operational challenge that can put railroads into a chaos that can lead to hampered operations, congested networks, and constrained volume growth. Canadian National (CN) has developed WinterREADY, a detailed plan to prepare for winter weather challenges. The plan is designed to promote network fluidity in extreme weather conditions in an effort to improve network capacity and resilience. The objective of the railroad’s response plan is to ensure continual goods movement by having alternate plans for key terminals and yards, readying cold weather detour schedules, and redirecting workflow to improve response time. CN said the rigorous response plan was the result of listening to customers to learn and adapt to a better position by adding resiliency and better, more timely communication throughout the network. In addition to major capacity enhancements and productivity initiatives, the railroad has augmented its snow fighting equipment fleet, adding backup generators with fail-over and auto start features, and providing new lifting equipment at key locations. In addition, the railroad has developed a strategy to make better use of power when an event occurs. Highlights of the power strategy include performing a winter maintenance blitz on all system locomotives, introducing high-capacity alternating current locomotives, and upgrading locomotives to prevent snow ingestion and protect radiators from snow accumulation.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.166 | 0.058 |
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".