Toxic Cargo: How Rail Transport of Vinyl Chloride Puts Millions at Risk, an Analysis One Year After the Ohio Train Derailment
Bibliographic record
Abstract
On February 3, 2023, five train cars containing 887,400 pounds of vinyl chloride, the key building block for polyvinyl chloride (PVC) plastic, derailed and burned, setting off a major environmental health disaster that sickened area residents and first responders, killed wildlife, and contaminated East Palestine, Ohio and surrounding communities. OxyVinyls is the largest vinyl chloride monomer producer in the United States and the third-largest PVC supplier in the United States. How much of this hazardous chemical is transported every year, and how many people are put at risk? To better understand the magnitude of this hazard, we established the most likely rail routes for shipping of vinyl chloride from two OxyVinyls plants in Texas to four PVC factories in New Jersey, Illinois, and Ontario. We estimate that up to 36 million pounds of vinyl chloride travels on more than 200 rail cars across nearly 2,000 miles of US railways at any given moment. Over a year, an estimated 8,595 rail cars carry approximately 1.5 billion pounds of vinyl chloride from OxyVinyls to these plastics plants. The rail shipment of vinyl chloride to make PVC plastic puts more than three million people at risk. We estimate more than three million people live, and about 670,000 children attend more than 1500 schools, within one mile of the train route between Texas and New Jersey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".