MétaCan
Menu
Back to cohort
Record W7134507281

Science of 'Sully'

2016· article· W7134507281 on OpenAlexaboutno aff
Matt Edison

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2016
Typearticle
Language
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAviationAerospaceOn boardAccident (philosophy)Accident 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 Nick 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. The 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. As 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. Wilson remembers the incident well. He also recalls discussing assessments of the incident with his colleagues as they watched the story unfold on national television. “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.” Rare incident Wilson says the National Transportation Safety Board and FAA took a long hard look at the checklists and procedures associated with the incident. One 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. The 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. Seeing 'Sully' Though 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. When speaking to people who aren’t pilots, Wilson offers advice on how to best enjoy the film: “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.’” Judgment call Flight 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. Sullenberger 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. Wilson commends Sullenberger on his decision. “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. Interestingly, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0830.034

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 head0.013
GPT teacher head0.189
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Explore more

Same venueUND Scholarly Commons (University of North Dakota)Same topicAir Traffic Management and OptimizationFrench-language works237,207