Global media lines up to interview UND's George Bibel following following AirAsia crash
Bibliographic record
Abstract
Global media lines up to interview UND's George Bibel following following AirAsia crash Shortly after authorities earlier this week acknowledged that AirAsia Flight 8501, an Airbus A320 airliner with 162 people aboard, was lost, University of North Dakota’s George Bibel got an email from Fox News. Bibel, professor of mechanical engineering in the College of Engineering & Mines, wasn’t surprised by that email and he’s not surprised by subsequent media calls related to that tragedy. Today (Dec. 31), for example, he’ll be on the air with Prairie Public Broadcasting, answering more questions about the AirAsia flight. “I’m in the media rolodex,” says Bibel, who’s book, Beyond the Black Box: the Forensics of Airplane Crashes, got noticed in aviation and media circles shortly after its publication in 2009. Click here to view Bibel’s interview with Fox News. Bibel’s subsequent book about train wrecks also got noticed ? and also put him in the spotlight for media calls following wrecks, such as the 2013 oil train derailment in Lac-Mégantic, Quebec, and the commuter train crash in New York City later that same year. Bibel, who’s an expert in mechanical failure and teaches a course in engineering disasters, suggests that what the media are looking for is answers. Even Boeing invited Bibel to speak at their headquarters several times after his book was published. “What there aren’t a lot of is simple explanations for disasters such as this week’s AirAsia crash,” he said. “That’s because mostly engineers who build airplanes are all highly specialized ? no one person can know all the complex details about modern aircraft because it takes thousands of people to design and build them.” Bibel developed his keen interest in such disasters ? he’s now working on a second book about aviation crashes ? because he wanted to enhance the content of his engineering classes. “Students are most interested in real-world examples, and in reading through aviation crash investigative reports, I discovered a lot of material that pertained directly to my classes,” Bibel said. “This kind of material definitely made my engineering lectures more interesting for students.” Among the artifacts in the CEM’s mechanical engineering lab is a solid aerospace-quality titanium disc, donated to Bibel by an engine manufacturer; it’s identical to a disc that failed in a United Airlines Flight 232 in 1989, causing a fiery crash near Sioux Falls, S.D. Bibel is getting asked a lot of questions, but, he said, it’s too early to know exactly what happened to the AirAsia flight last Sunday, when it disappeared from air traffic control radar screens on its way from Indonesia to Singapore. “For sure, it’s too soon to tell,” he said. “But the questions will keep coming until investigators finish their work,” a process that could take a couple of years. Juan Miguel Pedraza University & Public Affairs writer
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.139 | 0.050 |
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".