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Record W1579846757 · doi:10.5772/33113

Knowledge Integration to Support Networking for Laboratory Preparedness and Response to Emerging Pathogens

2012· book-chapter· en· W1579846757 on OpenAlexaff
N. Shamir, L P - King, Imbert Theodore, May Chu

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsPreparednessComputer scienceKnowledge managementPolitical science

Abstract

fetched live from OpenAlex

Laboratories play a critical role in facilitating timely recognition of and response to public health threats. However, capabilities and capacities vary widely among laboratories around the world. The scientific community recognizes that: 1) no single laboratory or network can effectively cover all health hazard threats and 2) connecting laboratories through networks enables scientific communities to harness and contribute their expertise in response to public health threats, while adding value and enhancing opportunities to enrich their own work. However, a consolidated and accessible inventory of laboratories that would enable this to happen does not exist. Public health laboratories serve the essential function of identifying etiologic agents of disease in an accurate and timely manner. However, the practicality and potential of these laboratories in the detection, monitoring, and reporting of threats over a wider geographic range is limited by unclear case definitions, inadequate laboratory capacity, and often, limited political will of local authorities to comply with International Health Regulations (IHR) 2005 Furthermore, some countries are not member states of World Health Organization (WHO) and therefore have no obligation to comply with IHR. Global, regional, and national laboratory networks serve to alleviate these issues by streamlining the detection, monitoring, and reporting procedures for communicable diseases in order to effectively and significantly reduce the global or regional burden of disease. Laboratory networks are useful in establishing and maintaining standards such as molecular disease confirmation by providing member laboratories with standardized testing and reporting procedures, reagents, equipment, training, reference materials, quality control indicators, and technical support. Such collaboration between and among laboratories facilitated by networks allows for rapid and accurate provision of information regarding the magnitude of disease and the strains that are circulating in particular regions, leading to faster response and more effective control of the threat. Some example networks include the Global Polio Network

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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