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Record W2992824656

The Effectiveness of Transitional Speed Zones

2004· article· en· W2992824656 on OpenAlexaboutno aff
Eric Hildebrand, Alan Ross, K Robichaud

Bibliographic record

VenueITE journal · 2004
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDispersion (optics)DaylightWave speedGeologyMeteorologyEnvironmental scienceGeographyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The effectiveness of transitional speed zones was reviewed in the province of New Brunswick, Canada, by collecting speed data as vehicles moved through transitional zones. At the onset, a premise of the study was that transitional speed zones might not be effective in reducing mean speeds and might, in fact, increase speed dispersion. Speeds were measured at five locations for sites with transitional zones and at three locations for sites without transitional zones. Data were collected during daylight hours and under good weather conditions. Analysis of the data revealed that signs are not effective in reducing speeds if there is not a corresponding change in highway characteristics. The transitional speed zones were found not to significantly impact the variance in speeds as vehicles traveled from the high speed zone. Overall, results indicate that there are not enough positive impacts associated with the provision of transitional speed zones to justify their use by road authorities. Language: en

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)0.004
metaresearch head score (Gemma)0.018
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 score0.179
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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