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Record W7098651104

A FAIR MODAL SHARE FOR CYCLING: TWENTY PERCENT BY 2020 IN ORLANDO By

2003· article· en· W7098651104 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureMetropolitan areaQuarter (Canadian coin)Public transportModalCycling
DOInot available

Abstract

fetched live from OpenAlex

This proposal advocates committing Orlando to becoming the first metropolitan area in the United States that develops a bicycle trail and off-road path system which results in 20 % of all trips by bicycle, or as transportation professionals say, in cycling assuming 20 % of the modal share, the proportion of travel by a particular mode such as cycling, public transport, driving, or walking. Over the last decade, the cycling rate in Orlando has remained at an officially guesstimated one-half of one percent of all trips; it seems likely the present `rate is actually less than a quarter of a per cent. During the same period, the volume of vehicular traffic increased exponentially, bringing our automobile use far beyond what is economically, socially, and environmentally optimal. Increasing the modal share for cycling will move us toward a balanced multimodal transportation system, providing many economic, social, and environmental benefits that our 96 % monomodal system does not. It will support President Bush's national initiative to reverse dependence on foreign oil (White House, 2003, January 28) as well as a long-term strategic plan for our diverse and

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0300.005

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 head0.030
GPT teacher head0.211
Teacher spread0.181 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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