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Annual costs of treating children aged 2–14 y by countries grouped by prevalence (see Table 3) and depending on the frequency of treatment given according to current WHO recommended thresholds [19] and the new three-tier thresholds proposed here.

2015· dataset· en· W6960231103 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2015
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsTable (database)PopulationQuarter (Canadian coin)Age groupsRange (aeronautics)Current Population Survey

Abstract

fetched live from OpenAlex

<p>Notes. The population estimates are for children aged 0–14 y from ref. <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000402#pntd.0000402-United1" target="_blank">[29]</a> converted to age range 2–14 y using the best approximation possible taken from the WHO Life Tables <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000402#pntd.0000402-WHO9" target="_blank">[32]</a> as follows. The number of person-years lived, <sub>n</sub>L<sub>x</sub> was applied to obtain total person-years lived from zero to below the age of 15 y (<sub>15</sub>L<sub>0</sub>) and from this was subtracted the number of person-years lived below the age of 1, one quarter of the person-years lived between 1 and 5, and one fifth of the person-years lived between 10 and 15, to obtain the proportion of the under-15 population who are at least 2 years old and 14 y or under: i.e. <sub>12</sub>L<sub>2</sub> = <sub>15</sub>L<sub>0</sub>−(<sub>1</sub>L<sub>0</sub>+0.25*<sub>4</sub>L<sub>1</sub>+0.2*<sub>5</sub>L<sub>10</sub>). The underlying data are provided in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000402#pntd.0000402.s001" target="_blank">Table S1</a>. Note that the estimates of percentages spent on initially-infected individuals, and those initially infected with 10+ worms, are based on static prevalence, and do not take into account declining prevalence with re-treatment. A dynamic model would be desirable.</p>

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)0.001
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.070
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.281
Teacher spread0.241 · 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
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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