MétaCan
Menu
Back to cohort
Record W4249060889 · doi:10.22215/etd/2015-10785

Modelling and Synthesis of High Speed Craft Acceleration Profiles

2015· dissertation· en· W4249060889 on OpenAlexaff
Mohamed El Tayeby Ahmed

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsCarleton UniversityDefence Research and Development Canada
Fundersnot available
KeywordsAccelerationShock (circulatory)Parametric statisticsMarine engineeringSimulationEngineeringComputer scienceMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

High Speed Craft (HSC) occupants are subjected to a harsh environment characterized by vibration and repeated shock.Significant research has therefore been invested into HSC shock mitigation.The objective of this work was to determine standard shock acceleration profiles for HSC based on sea trial data.The knowledge of real-life sea inputs is important for in-lab testing of HSC shock mitigation seats.Sea trial vertical acceleration data of a Rigid-Hull Inflatable Boat was analyzed with the aim of characterizing shock acceleration pulses experienced by the boat.Five common types of shock acceleration profiles were identified.These profiles were represented by a combination of modified Gaussian curves.The statistical characteristics of the observed pulses were obtained by curve fitting the data.An algorithm employing copulas and non-parametric density estimation was developed for generating synthetic time histories which have the same statistical characteristics as the observed data.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.243
Teacher spread0.220 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicWind and Air Flow StudiesFrench-language works237,207