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Record W633210036

TORONTO INT'L AIRPORT INSTALLS POF PARKING REVENUE CONTROL SYSTEM

2003· article· en· W633210036 on OpenAlexaboutno aff
Robert J. Duffy

Bibliographic record

VenueParking Today · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTicketPaymentCredit cardRevenueBusinessDebit cardSmart cardTransport engineeringFinanceAdvertisingComputer securityComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Toronto Pearson International Airport's Terminal 3 has ten vehicle exit, barrier gate lanes. Still, long lines at these exit lanes left unhappy customers inhaling vehicle exhaust fumes. The solution was to install 16 Pay-On-Foot Stations and 10 Credit Card Express Exit Terminals. So far, the results have been positive. The percentage of transactions processed at automated payment stations is increasing, reaching approximately 55 percent by early March 2003. Total payments processed by credit card had doubled against the credit card payments processed at cashier lanes. The number of cashier lanes had been reduced from 10 to five. The toughest challenge was to educate travelers to take their parking ticket with them instead of leaving it in their vehicles.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.266
Teacher spread0.252 · 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 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

Citations0
Published2003
Admission routes1
Has abstractyes

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