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Record W1873182176 · doi:10.1177/003335490812300516

A Multiagency Effort to Reduce Bicyclist Fatalities and Serious Injuries in New York City

2008· article· en· W1873182176 on OpenAlexaboutno aff
Jenna Mandel-Ricci, Catherine Stayton, Leze Nicaj, Solomon Assefa, David Woloch, Kevin Jeffrey, Patrick McCarthy, Noah Budnick

Bibliographic record

VenuePublic Health Reports · 2008
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
FundersNew York City Department of Health and Mental Hygiene
KeywordsOccupational safety and healthPoison controlInjury preventionSuicide preventionMedicineHuman factors and ergonomicsEnvironmental healthMedical emergencyGerontologyPathology

Abstract

fetched live from OpenAlex

In the summer of 2005, responding to an apparent increase in the number of bicyclist fatalities in New York City (NYC), a coalition of bicycling advocacy groups, clubs, racing organizations, and working cyclists presented a six-point Bike Safety Action Plan to NYC's Department of Transportation (DOT) and Police Department (NYPD).Leading the list of desired action steps was the coalition's request for a comprehensive study of bicyclist fatalities.The requested report was to be modeled on a bicycling casualty study completed by the city of Toronto in the late 1990s, 1 considered by many advocates to be the most comprehensive municipal study to improve bicycling conditions to date.Advocates asked that the city's health department play a role in the study.One year later, in September 2006, four NYC agencies with a shared interest in promoting safe bicycling-the Department of Health and Mental Hygiene (DOHMH), Department of Parks and Recreation, DOT, and NYPD-released a joint report to the public entitled Bicyclist Fatalities and Serious Injuries in New York City: 1996City: -2005. 2 . 2 The report provided a comprehensive examination of bicyclist fatalities and serious injuries, drawing on multiple data sources including police accident reports, accident scene investigations, and medical examiner files.As part of the report, the city announced an aggressive package of action steps to increase bicycling and promote safety.Details of the investigation, proposed action steps, and subsequent efforts are described in this article.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.117
GPT teacher head0.391
Teacher spread0.275 · 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 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

Citations5
Published2008
Admission routes1
Has abstractyes

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