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Record W7043859224

Tanzanian Women And Babies Study Shows How To Improve Mosquito Net... - 2 February 2010

2010· other· en· W7043859224 on OpenAlexaboutno aff

Bibliographic record

VenueLSHTM Research Online (London School of Hygiene and Tropical Medicine) · 2010
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaHygienePopulationDar es salaamMalariaGovernment (linguistics)Public healthTropical medicineHealth care
DOInot available

Abstract

fetched live from OpenAlex

Tanzanian Women And Babies Study Shows How To Improve Mosquito Net Use To Protect Against Malaria
\n
\nTanya Marchant of the London School of Hygiene and Tropical Medicine tells Peter Goodwin about her research findings on how to improve the prevention of malaria. According to her study—conducted jointly with colleagues from the Ifakara Health Institute in Dar es Salaam and published in the Canadian Medical Association Journal looking at pregnant women and babies in Tanzania—insecticide-treated bed-nets (which prevent mosquitoes transmitting the malarial parasite) can be more effective if care and thought is given to how to distribute them and educate communities, and if the nets are treated with insecticide during manufacture, or at least before they are given to the public.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.353
Teacher spread0.315 · 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
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
Published2010
Admission routes1
Has abstractyes

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