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Record W2052377347 · doi:10.1890/090066

Migratory Canada geese cause crash of US Airways Flight 1549

2009· review· en· W2052377347 on OpenAlexaboutno aff
Peter P. Marra, Carla J. Dove, Richard A. Dolbeer, Nor Faridah Dahlan, Marcy Heacker, James F. Whatton, Nora E. Diggs, Christine A.M. France, Gregory Henkes

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2009
Typereview
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersMuseum Conservation InstituteSmithsonian Institution
KeywordsBiological dispersalCrashTakeoffAeronauticsGeographyFlight featherCivil aviationAviationEcologyBiologyEngineeringComputer scienceAerospace engineeringMedicineEnvironmental health

Abstract

fetched live from OpenAlex

In the United States alone, over 7400 bird–aircraft collisions (birdstrikes) were reported in 2007. Most of these strikes occurred during takeoff or landing of the flight, and it is during these flight phases that aircraft experience their highest risk of substantial damage after colliding with birds. Birdstrikes carry enormous potential costs in terms of lives and money. Using feather remains and other tissue samples collected from the engines of US Airways Flight 1549, which crash landed in the Hudson River in New York City on 15 January 2009 after a birdstrike, we apply molecular tools and stable hydrogen isotopes to demonstrate that migratory Canada geese were responsible for the crash. Determining whether the geese involved in this birdstrike event were resident or migratory is essential to the development of management techniques that could reduce the risk of future collisions. Currently, the US civil aviation industry is not required to report birdstrikes, yet information on frequency, timing, and species involved, as well as the geographic origin of the birds, is critical to reducing the number of birdstrikes. Integrating this information with bird migration patterns, bird‐detecting radar, and bird dispersal programs at airports can minimize the risk of such collisions in the future.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations90
Published2009
Admission routes1
Has abstractyes

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