WHAT RECENT HHFT DERAILMENT FIRES TELL US
Bibliographic record
Abstract
ABSTRACT ID: 2017-145. In July 2016, TRC Environmental Corporation (TRC) and Hildebrand and Noll Associates, Inc. (HNA) were requested to develop planning guidance on train derailments involving large volumes/high concentrations of denatured ethanol for the Massachusetts Emergency Management Agency (MEMA). As part of this project, as well as similar projects conducted by HNA for other clients, TRC and HNA assessed current firefighting strategies for the release of ethanol and/or crude oil from High Hazard Flammable Trains (HHFT) and developed the planning assumptions necessary to prepare for these types of incidents. For these projects, studies and in-depth analyses of 27 HHFT derailments resulting in tank cars breaches that occurred in the United States and Canada involving denatured ethanol1 (ethanol) and/or crude oil2 from 2006 through 2015 were performed. The analyses were primarily based on the information from the National Transportation Safety Board (NTSB), Federal Railroad Administration (FRA), and/or Transport Canada (TC) databases, with supplemental information from news reports in some cases. The objective of these analyses was to identify key planning assumptions that would be used in developing appropriate firefighting strategies by focusing on the number and types of cars derailed, approximate train speeds at the time of the derailment, number of cars breached, amount of product released, and whether or not the released product caught fire. Additionally, the studies included obtaining and reviewing information on the properties and characteristics of ethanol, crude oils, and other Class 3 flammable materials, as well as information for railroad tank cars. Insights and understandings gained from these studies were used to further develop the firefighting strategies for HHFT derailment fires.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".