Bibliographic record
Abstract
In 2005, 43 Canadian National Railway (CN) oil cars derailed into an Alberta lake, spilling about 194,000 gallons of bunker crude and pole-treating oil. This article questions the response of both the railway and the government in responding to this incident. Although CN will have to bear all the clean-up costs and may be charged under several statutes for the incident, this derailment appears to be part of a disturbing new pattern. Within one month of the Alberta incident, there were 3 other derailments, two with spills of hazardous materials. CN's allegedly poor response to the Alberta incident calls into question their commitment to the Canadian Chemical Producers' Responsible Care program, which requires members such as CN to have an up-to-date operational transportation emergency response plan, contain and clean up releases, provide technical advisors at accident scenes and assist local emergency response forces. Although industry has the primary responsibility of protecting the citizenry against incidents arising out of the manufacture and transport of hazardous materials, government also has a crucial monitoring and enforcement role. The increase in railroad incidents suggests that the Canadian Transportation Safety Board needs to take a larger role in holding the responsible carriers to account.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.002 |
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".