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Record W2255663632

Babes in Arms: The Safety of Infants and Small Children on Commercial Aircraft

2016· article· en· W2255663632 on OpenAlexaboutno aff
Christina M. Rudin-Brown, Gayle Conners

Bibliographic record

VenueTransportation Research Board 95th Annual MeetingTransportation Research Board · 2016
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsTakeoffAviationAeronauticsCrashLegislationCommercial aviationOccupational safety and healthProcurementAviation safetyEngineeringBusinessTransport engineeringMedicinePolitical scienceComputer scienceAutomotive engineeringMarketing
DOInot available

Abstract

fetched live from OpenAlex

The death of a lapheld infant in an otherwise survivable crash landing in 2012 in Canada’s north prompted a review of commercial aviation child restraint practice and legislation. Because they can prevent injury and death in survivable aviation occurrences, seatbelts are required for passengers over the age of two during taxi, takeoff, approach and landing, and during periods of turbulence. Carry-on luggage and other items are also required to be stowed during these periods of flight to limit their potential to cause injury. Despite these safeguards, current regulations in North America and elsewhere allow infants and small children to be carried on an adult’s lap without restraint by a seatbelt or other device, exposing them to an unnecessarily high level of risk and depriving them of a level of safety provided to adults and older children. At the same time, estimating the level of risk exposure of these inadequately restrained passengers is difficult because there are no requirements for airlines to collect or report data on the number travelling. An informal survey of four commercial air transport operators revealed that infants and children make up 13 to 14 percent of their total passenger loads. Lessons learned during the evolution of child passenger safety in the road environment must be translated to the commercial aviation setting so that the youngest passengers can be afforded the level of safety they deserve. This can be accomplished through the development of innovative and practical age- and size-appropriate child restraint systems (CRS).

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)0.006
metaresearch head score (Gemma)0.001
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 score0.081
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.369
Teacher spread0.312 · 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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