MétaCan
Menu
Back to cohort
Record W2072598740 · doi:10.1002/atr.102

Analysis of work trips made by taxi in canadian cities

2010· article· en· W2072598740 on OpenAlexaffvenueabout
Lina Kattan, Alexandre de Barros, S. C. Wirasinghe

Bibliographic record

VenueJournal of Advanced Transportation · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTRIPS architectureTaxisPublic transportTransport engineeringDisadvantagedWork (physics)Regression analysisPopulationGeographyJourney to workTransit (satellite)Demographic economicsStatisticsDemographyEconomic growthEconomicsEngineeringMathematicsSociology

Abstract

fetched live from OpenAlex

Abstract This paper presents two regression models for work trips made by taxi for the year 1996 and the year 2001, respectively for 25 Canadian cities. These regression models indicates the primary factors that influence work commuting by taxi. Two major factors are identified: the total number of work trips made by public transit and the total number of low‐income households. The 2001 regression model indicates an increase of the value of the transit commuter's coefficient from its 1996 figure. These results highlight the important role that taxis play in: (i) decreasing the demand for parking especially in urban cores and (ii) serving the transportation disadvantaged population especially in outlying areas poorly served by public transport. Copyright © 2010 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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 designObservational
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

Citations23
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Advanced TransportationSame topicUrban Transport and AccessibilityFrench-language works237,207