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Record W2162096362

SARS, Pandemics and Public Health

2010· article· en· W2162096362 on OpenAlexaff
Julia Skelding, Ross Upshur

Bibliographic record

VenueIntegrated Assessment · 2010
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPandemicPublic healthInfectious disease (medical specialty)Emerging infectious diseaseGlobalizationCivilizationDiseaseDevelopment economicsPolitical scienceCoronavirus disease 2019 (COVID-19)Economic growthGeographyMedicineEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

Emerging and re-emerging infectious diseases are newly or previously identified diseases that are increasing in incidence or changing in geographic range (Lederberg et al., 1992). Severe acute respiratory syndrome (SARS) and avian influenza are two of the most prominent recent examples of such diseases. It has been apparent for the better part of two decades that a host of interacting factors are causally linked to this emergence, including ecological changes, changes in human demographics and behaviour (particularly the explosion of air travel in the past twenty years), technology and industry, and microbial adaptation and change. More importantly, deficiencies in public health infrastructure coupled with globalization have diminished the capacity of public health systems to respond adequately to the threat of infectious diseases. Authoritative scholars have issued warnings about viral emergence and detailed the steps necessary for civilization to respond. These seemingly dire and apocalyptic warnings have, in fact, come partly true. The emergence of infectious disease is an enduring aspect of human existence, one neglected at our peril.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.040
GPT teacher head0.377
Teacher spread0.337 · 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 designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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