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Record W2474657891 · doi:10.2172/1097170

Electric Ground Support Equipment Advanced Battery Technology Demonstration Project at the Ontario Airport

2013· report· en· W2474657891 on OpenAlexaboutno aff
Tyler Gray, Jeremy Diez, Jeffrey Wishart, J.E. Francfort

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentBattery (electricity)Work (physics)EngineeringLead–acid batteryTransport engineeringEmerging technologiesAeronauticsSystems engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The intent of the electric Ground Support Equipment (eGSE) demonstration is to evaluate the day-to-day vehicle performance of electric baggage tractors using two advanced battery technologies to demonstrate possible replacements for the flooded lead-acid (FLA) batteries utilized throughout the industry. These advanced battery technologies have the potential to resolve barriers to the widespread adoption of eGSE deployment. Validation testing had not previously been performed within fleet operations to determine if the performance of current advanced batteries is sufficient to withstand the duty cycle of electric baggage tractors. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. The demonstration project also grew the relationship with Southwest Airlines (SWA), our demonstration partner at Ontario International Airport (ONT), located in Ontario, California. The results of this study have encouraged a proposal for a future demonstration project with SWA.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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
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

Citations1
Published2013
Admission routes1
Has abstractyes

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