Norfolk Southern CEO, Alan H. Shaw, appeared before the Senate Commerce Committee to address concerns regarding the recent train derailment and hazardous material spill in East Palestine, Ohio. Shaw announced that the company would provide financial support to the residents of the affected area, including a medical compensation fund to address any long-term health risks associated with the accident and a property value assurance program. The hearing was convened to examine the derailment and regulatory changes that could be implemented to prevent similar incidents in the future.
The commitments made by Norfolk Southern come in the wake of intense pressure from lawmakers in both parties to do more to support the community of East Palestine. Shaw also voiced his support for a bipartisan bill introduced after the derailment, which seeks to tighten rail regulations, including strengthening notification and inspection requirements for trains carrying hazardous materials and increasing fines for safety violations by rail carriers. “We support legislative efforts that use science and data to enhance the safety of the freight rail industry,” said Shaw.
However, not all witnesses shared Shaw’s support for the stricter regulations. Ian Jefferies, the CEO of the Association of American Railroads, argued in written testimony that the legislation would place “excessive and unnecessary operational burdens” on the industry. Senator J.D. Vance of Ohio, the lead Republican behind the bill, called out the railroad industry for opposing it, stating, “To the rail industry: Don’t lie about my bill. Don’t slander the staff who drafted it.”
Senator Edward J. Markey, Democrat of Massachusetts, expressed his frustration with industry experts talking about supporting the principles of regulation while lobbying against common-sense regulations like this one behind the scenes. Three Senate Republicans co-sponsored the bill when it was first introduced, but it remains unclear whether it has enough support to advance in the closely divided Senate, where most major legislation needs 60 votes to move forward, or whether the measure could draw support in the Republican-led House.
Shaw stated that his company backed the proposed changes, including stricter standards for freight car safety, better real-time information for emergency medical workers about the contents of trains moving through their communities, and requirements to phase out older tank car models and develop better early-warning sensor technologies and advanced tank car design. Safety experts believe that the derailment could have been averted had Norfolk Southern placed detectors closer together on the route the train took.
The National Transportation Safety Board (NTSB) opened a special investigation into the company’s safety practices this month. Since December 2021, Norfolk Southern has suffered five significant accidents, and another derailment occurred hours before a Senate Environment and Public Works Committee hearing this month. Senator Ted Cruz, Republican of Texas, questioned why Norfolk Southern did not stop the train immediately after a sensor showed that the wheel was overheating. Shaw responded that the temperature measured by an intermediary sensor was not high enough by the company’s standards to stop the train. He said the company has since lowered the benchmark from 200 degrees over ambient temperature to 170 degrees.
During the hearing, Clyde Whitaker, the Ohio state legislative director for the transportation labor union SMART Transportation Division, filed a complaint with the Federal Railroad Administration in July about an unsafe practice at Norfolk Southern. Whitaker alleged that the company instructed crews to disregard failures of wayside detectors, which monitor the temperature of rail car parts. “This meant that the trains were not being inspected as intended, and that the crews were not able to ascertain the integrity of their trains,” said Whitaker. He also criticized the company’s practice of precision scheduled railroading, which involves cutting back on rail yard workers, inspectors, and equipment to adhere to stricter train schedules to maximize profits.
Perplexity refers to the level of uncertainty or unpredictability in a text. It measures how well a language model can predict the next word in a sequence based on the previous words. A lower perplexity score indicates that the language model can more accurately predict the next word in the sequence.
In the news article, there are several instances where the language model may encounter perplexity due to the technical terms used in the context of the rail industry. For example, the language model may have difficulty predicting the next word after “real-time information for emergency medical workers about the contents in the trains that are moving through their communities” due to the complexity of the technical language. Similarly, the use of terms like “tank car models” and “precision scheduled railroading” may also cause perplexity for the language model.
Burstiness refers to the level of variability or irregularity in the occurrence of words in a text. In other words, it measures how often certain words or phrases appear in relation to other words or phrases. A text with high burstiness has a few words or phrases that occur frequently, while the rest of the words occur less frequently.
In the news article, there are several instances of burstiness, where certain words or phrases occur more frequently than others. For example, the words “Norfolk Southern” and “derailment” occur multiple times throughout the article, indicating a bursty pattern. Similarly, the phrase “rail regulations” appears several times, indicating its importance in the context of the article. The name “Senator J.D. Vance” also appears several times, indicating his prominent role in the discussion of the bipartisan bill.
In conclusion, perplexity and burstiness are important concepts in natural language processing and information theory. Perplexity measures how well a language model can predict the probability of a given sequence of words, while burstiness refers to the tendency of certain words to occur in clusters or bursts rather than being evenly distributed throughout a text. Both concepts have practical applications in various fields, including machine learning, linguistics, and communication studies. Understanding these concepts can help researchers and practitioners to develop more accurate language models, analyze text data more effectively, and communicate more clearly and persuasively.