MétaCan
Menu
Back to cohort
Record W4393849291 · doi:10.5281/zenodo.3819126

Coronavirus disease (COVID-19) case data - South Africa

2020· dataset· en· W4393849291 on OpenAlexaff
Vukosi Marivate, Riaz Arbi, Herkulaas Combrink, Alta de Waal, Henkho Dryza, Derrick Egersdorfer, Shaun Garnett, Brent M. Gordon, Lizel Greyling, Ofentswe Lebogo, Dave Mackie, Bruce Merry, S'busiso Mkhondwane, Mpho Mokoatle, Shivan Moodley, Jabu Mtsweni, Nompumelelo Mtsweni, Paul Myburgh, Jannik Richter, Vuthlari Rikhotso, Simon Rosen, Joseph Sefara, Anelda van der Walt, Schalk van Heerden, Jay Welsh

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CoronavirusVirologyPandemicGeographyDiseaseMedicineInfectious disease (medical specialty)OutbreakPathology

Abstract

fetched live from OpenAlex

COVID 19 Data for South Africa created, maintained and hosted by DSFSI research group at the University of Pretoria\n\nDisclaimer: We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know. \n\nSee original GitHub repo for detailed information https://github.com/dsfsi/covid19za

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0270.031

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 head0.447
GPT teacher head0.435
Teacher spread0.012 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicCOVID-19 and healthcare impactsFrench-language works237,207