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Record W1967350079 · doi:10.1061/40530(303)16

Location of New Large Aircraft Gate Positions in Pier Terminals

2000· article· en· W1967350079 on OpenAlexaff
Alexandre G. de Barros, S. C. Wirasinghe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPierTerminal (telecommunication)EngineeringTransport engineeringTransfer (computing)Computer scienceMarine engineeringAeronauticsTelecommunicationsCivil engineering

Abstract

fetched live from OpenAlex

The advent of the New Large Aircraft (NLA) will impose a series of problems on airports. Most existing airports were not designed to accommodate aircraft larger than the Boeing 747, and new airports will have to be designed so as to cope with NLA. The location of NLA gate positions in the passenger terminal is of importance to landside operations. Two of the most important parameters of terminal design efficiency — passenger walking and baggage transfer distances — will be directly affected by the location of the NLA gates. This paper will provide a discussion on the location of NLA gates in the passenger terminal. Based on the proportions of hub transfer passengers, a mathematical formulation is derived for pier terminals to help in the choice of the specific location of NLA gates that will minimize passenger walking distance. Two basic cases of pier terminals are considered: access to the main building through the pier base; and access to the main building via the middle of the pier.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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 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

Citations6
Published2000
Admission routes1
Has abstractyes

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