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Record W572366602

H1N1: Are You Ready?

2009· article· en· W572366602 on OpenAlexaboutno aff
Kathy Scott

Bibliographic record

VenueMass transit · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportTransit (satellite)CommissionBusinessProduct (mathematics)PandemicShut downAdvertisingEngineeringTransport engineeringCoronavirus disease 2019 (COVID-19)MedicineFinance
DOInot available

Abstract

fetched live from OpenAlex

This article describes the invasiveness of H1N1 and other germs in transit systems and what is being done to protect passengers and employees. Public transportation is viewed as a “pandemic vector” because it enables germs to spread quickly and efficiently as school children, teachers, healthcare workers, public safety workers, and others travel back and forth to schools or businesses. The author notes that schoolchildren in Melbourne, Australia acted as “super spreaders” once schools were finally shut down; children returned to their communities where diagnoses of H1N1 quickly accelerated. Beyond stressing hygiene etiquette, transit systems have substantially stepped up their disinfecting regimens. The author describes Bombardier’s Healthy Transit Program product, which the Toronto Transit Commission has purchased to protect the interiors of its new subway cars. The product is used as an electrostatic fogger that coats the interior of the transit vehicle with disinfectant.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.002

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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

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
Published2009
Admission routes1
Has abstractyes

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