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Record W1604506016

Independent Mobility for Older People

2004· article· en· W1604506016 on OpenAlexaboutno aff
Christopher Mitchell

Bibliographic record

Venue10th International Conference on Mobility and Transport for Elderly and Disabled PeopleJapan Society of Civil EngineersTransportation Research Board · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsOlder peopleIndependence (probability theory)Public transportPedestrianQuarter (Canadian coin)GeographyFoot (prosody)GerontologyTransport engineeringEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

In European countries, walking provides for about a quarter of all journeys and local buses provide useful mobility, particularly in larger urban areas. Between the ages of about 60 and 75 the average number of journeys on foot and by bus increases or is at least constant, while the number of journeys as a car driver reduces. In Britain, people aged over 70 make more journeys on foot than as car drivers, except in deep rural areas. In the United States of America (USA), walking only accounts for some 8% of all journeys and local buses account for less than 2%. As people age, they walk and use buses less and become even more dependent on car travel than they were in middle age. The paper describes travel patterns for older persons in three European countries and the USA. It outlines how pedestrian infrastructure and local public transport can be made easy for older persons to use, enabling older people to retain their independence and mobility.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.053
GPT teacher head0.359
Teacher spread0.305 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

Explore more

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