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Record W7041000513

Science of 'Sully'

2016· article· en· W7041000513 on OpenAlexaboutno aff

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2016
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAviationAerospaceAccident (philosophy)On boardAccident investigationAviation medicine
DOInot available

Abstract

fetched live from OpenAlex

Assistant Professor at UND Aerospace discusses the story of an aircraft emergency landing on the Hudson River in 2009, now featured in a top box office motion picture \nNick Wilson, assistant professor of aviation at the University of North Dakota’s John D. Odegard School of Aerospace Sciences, has a unique connection to the emergency landing on the Hudson River of U.S. Airways Flight 1549 on Jan. 15, 2009.\nThe famous “landing” and courageous airline captain that made it happen are subjects of a major motion picture, “Sully,” currently No. 1 in the country, based on box-office returns.\nAs a systems and procedures instructor for the Airbus 320 (A320) — the aircraft flown by Airline Capt. Chesley Sullenberger — Wilson provided initial and continuing-qualification training for captains and first officers at a major airline for five years. Many of those aviators are still active crewmembers flying the A320 today.\nWilson remembers the incident well. He also recalls discussing assessments of the incident with his colleagues as they watched the story unfold on national television. \n“Whenever an incident or accident occurs,” Wilson says, “it’s really important not to make initial assessments without more detail. That’s a common outcome with the media.”\nRare incident\nWilson says the National Transportation Safety Board and FAA took a long hard look at the checklists and procedures associated with the incident.\nOne of the aspects that made this incident so unique was the dual-engine failure, which, at such low altitudes, is extremely uncommon. Because of the rarity, checklists associated with a dual-engine failures were not optimized, specifically procedures associated with “ditching” the aircraft.\nThe importance of including low-altitude engine failures in checklists is apparent when one considers the lack of safe places to force land an aircraft in heavily congested cities like New York City.\nSeeing 'Sully'\nThough Wilson has not been able to see “Sully” yet, he is looking forward to the opportunity. He’s eager to see how much detail of the incident the filmmakers covered.\nWhen speaking to people who aren’t pilots, Wilson offers advice on how to best enjoy the film:\n“One thing I would say to the general public watching the movie would be – ‘Take in as much of this experience as you can, because it certainly is going to be exciting,’” Wilson said. “’but also, research the topic in more detail. What I can’t comment on is how accurate the movie is compared to the realities that the flight crew faced. I’m certain they made an attempt to realize that and that’s the difference between making a movie and actually facing an emergency.’”\nJudgment call\nFlight 1549, piloted by Sullenberger, departed LaGuardia Airport in New York City just before 3:30 p.m. After climbing to 3,000 feet, the aircraft struck a flock of Canada geese, which caused damage to its engines and resulted in a loss of thrust for both.\nSullenberger opted to land in the nearby Hudson River after judging that he could not return to LaGuardia or any other airport. Through quick decision-making, Sullenberger and crew were able to safely guide the aircraft on to the Hudson with no loss of life.\nWilson commends Sullenberger on his decision.\n“The captain elected, and arguably very rightly so, to land in the Hudson where there would be the least potential loss of life, and it ultimately resulted in no loss of life, which is quite amazing,” Wilson said.\nInterestingly, the A320 involved in the Hudson River landing was equipped to handle an over-water landing. At the time of the incident, this was not common with the entire U.S. Airways fleet nor the fleets of many other major air carriers. This equipment was not a requirement for the route of Flight 1549, however, having passenger life vests and escape rafts on board ended up being advantageous in the rescue effort.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.177
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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