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Record W4297682779 · doi:10.1002/9781119830054.ch9

Other Transportation Case Studies

2022· other· en· W4297682779 on OpenAlexaboutno aff
Lee T. Ostrom

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsBARGETrainHazardous wasteAeronauticsTransport engineeringEngineeringForensic engineeringMarine engineeringGeographyCartographyWaste management

Abstract

fetched live from OpenAlex

Tour boats, cargo ships, military ships, and railroads have had disastrous accidents. Large marine vessels are less maneuverable than smaller vessels, are heavy, and can carry hazardous materials. When these vessels collide with other vessels or land, they can cause a great amount of damage. Duck boats are a very popular attraction, but many are World War II vintage and have not been upgraded to meet current safety standards that include proper amount of reserve bouncy. Accidents involving railroads are also quite common. Passenger trains, like commercial airliners, can carry hundreds of people and a major accident can cause numerous deaths. Trains also convey hazardous materials that can burn and/or explode. This chapter discusses the following accidents: LNG Barge – May 2019 USS FITZGERALD – June 2017 USS MCCAIN – August 2017 Stretch Duck 7 – July 2018 Amtrak – May 2015 Montreal, Maine, and Atlantic – July 2013

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 categoriesInsufficient payload (model declined to judge)
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 score0.165
Threshold uncertainty score0.835

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)0.1650.000

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 head0.015
GPT teacher head0.256
Teacher spread0.240 · 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 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
Published2022
Admission routes1
Has abstractyes

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