<scp>L</scp>ac‐<scp>M</scp>égantic accident: What we learned
Bibliographic record
Abstract
A tragic train derailment in Lac‐Megantic, a small Quebec community caused 47 fatalities, the destruction of part of the town and huge cleaning costs. The Transportation Safety Board of Canada (TSB) has conducted an in‐depth investigation of the causes of Lac‐Mégantic accident and has formulated recommendations. The catastrophic consequences of the Lac‐Mégantic accident and the known increase over the last several years in rail transportation of Class 3 hazardous materials has made it clear, the need to review the existing regulations and industry practices to such transportation. Canada Transport Safety Board, U.S. National Transportation Safety Board, Transport Canada, U.S. Pipeline and Hazardous Material Safety Administration, and U.S. Federal Railroad Administration are working closely to upgrade rail transport regulations to prevent similar incidents from occurring. The tragedy in Lac‐Mégantic was not caused by one single person, action, or organization. Many factors played a role, and addressing the safety issues will take a concerted effort from regulators, railways, the Association of American Railroads, shippers, tank car manufacturers, and refiners in Canada and the United States. © 2014 American Institute of Chemical Engineers Process Saf Prog 34: 2–15, 2015
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".