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Record W7045548756

Avian influenza the current state of affairs : the public health perspective in Canada / by Yvonne B. Kangong.

2017· other· en· W7045548756 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsInfluenza A virus subtype H5N1Public healthPandemicPerspective (graphical)State (computer science)DiseaseInfectious disease (medical specialty)Transmission (telecommunications)
DOInot available

Abstract

fetched live from OpenAlex

In light of Canada?s need to deal with respiratory infectious diseases such as
\ntuberculosis, Avian Influenza and severe acute respiratory syndrome (SARS), public
\nhealth departments across the country have been challenged with the need to develop
\naction plans for both treatment and prevention. Yet the problem is not isolated to Canada
\nbut is considered to be a major public health threat both in Canada and internationally.
\nThe emergence of viral respiratory infections in Canada may be attributed to several
\nsources, which include migration from one part of the world to another either for
\npleasure or business, as a major contributor. Such migrations coupled with inadequate
\ninfectious disease prevention strategies not only leads to rapid transmission of respiratory
\ninfectious diseases, but there is also a problem in administering effective treatment and
\nvaccines to infected cohorts.Several factors contribute to both the accidental and/or
\ndeliberate transfer of microbial agents. For example, economic, cultural and political
\ninteractions invoke the emergence of new and unrecognized microbial disease agents
\n(Lashley, 2006). New diseases have the potential to spread across the world in a matter of
\ndays, or even hours, making early detection and action more important than ever
\n(BCCDC, 2008).

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.410
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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