MétaCan
Menu
Back to cohort
Record W2235639886 · doi:10.1016/s0140-6736(16)00080-5

Anticipating the international spread of Zika virus from Brazil

2016· letter· en· W2235639886 on OpenAlexafffund
Isaac I. Bogoch, Oliver J. Brady, Moritz U. G. Kraemer, Matthew German, Marisa Creatore, Manisha A. Kulkarni, John S. Brownstein, Sumiko R. Mekaru, Simon I Hay, Emily Groot, Alexander Watts, Kamran Khan

Bibliographic record

VenueThe Lancet · 2016
Typeletter
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of OttawaSt. Michael's HospitalPublic Health OntarioUniversity of TorontoUniversity Health Network
FundersU.S. National Library of MedicineFogarty International CenterCanadian Institutes of Health ResearchScience and Technology DirectorateNational Institutes of HealthWellcome TrustU.S. Department of Homeland SecurityWellcomeBill and Melinda Gates Foundation
KeywordsZika virusVirologyGeographyMedicineVirus

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0330.029
Insufficient payload (model declined to judge)0.0030.002

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 head0.031
GPT teacher head0.310
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations476
Published2016
Admission routes2
Has abstractno

Explore more

Same venueThe LancetSame topicMosquito-borne diseases and controlFrench-language works237,207