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Record W4389192068 · doi:10.22215/etd/2023-15669

A Study of Vibration and Noise During Neonatal Patient Transport by Ground Ambulance

2023· dissertation· en· W4389192068 on OpenAlexaff
Patrick Kehoe

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsNoise (video)VibrationAccelerationGround transportationComputer scienceEngineeringTransport engineeringAcousticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Often, preterm infants requiring specialized care are transported to a higher care facility by air or ground ambulance. Studies have shown higher rates of mortality and morbidity in outborn patients, and concerns have been raised with respect to the vibration and noise environment during transport. To address these issues, Carleton University is working on a multi-year collaborative research project focused on three pillars: characterization of vibration and noise during transport, laboratory experimentation, and investigation of mitigation approaches. This study expands on existing characterization of vibration and noise by focusing on different road classififications and the impact of discrete events such as railway crossings and pothole strikes. In addition, a novel approach of full vehicle testing is demonstrated along with the evaluation of preliminary mitigation solutions. Finally, a method for determining the optimal location for a transport system or estimating acceleration values throughout an ambulance is presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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
Published2023
Admission routes1
Has abstractyes

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