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Record W6938956454 · doi:10.6068/dp170f3855af122

TREND: World Health Organization. Coronavirus Disease 2019 (COVID-19): Total Confirmed Coronavirus Cases | Country: Canada, 01/27/2020 - 03/18/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 091-001-001 World Health Organization. Coronavirus Disease 2019 (COVID-19): Total Coronavirus Related Deaths | Country: Canada, 01/27/2020 - 03/18/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 091-001-002

2020· other· en· W6938956454 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthCoronavirusMiddle East respiratory syndromeGlobal healthPublishingOutbreakEpidemiologyDisease

Abstract

fetched live from OpenAlex

World Health Organization. Coronavirus Disease 2019 (COVID-19): Total Confirmed Coronavirus Cases | Country: Canada, 01/27/2020 - 03/18/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 091-001-001 Dataset: Reports the cumulative count of reported cases confirmed as Coronavirus (COVID-19) worldwide by nation. WHO defines confirmed cases as a person with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms. Coronaviruses are a large family of viruses that may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019. WHO is publishing daily updates on the COVID-19 situation worldwide and offers guidelines and other information on its website: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 Many national and local public health agencies are also publishing location-specific detail. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ Category: Health and Vital Statistics, International Relations and Trade Subject: Epidemics, Epidemiology, Diseases Source: World Health Organization http://www.who.int/ The World Health Organization (WHO) is the directing and coordinating authority for health within the United Nations system. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. WHO was established in 1948. https://www.who.int/

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 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)0.001
metaresearch head score (Gemma)0.018
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.328
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.019
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0050.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1600.111

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 head0.051
GPT teacher head0.318
Teacher spread0.267 · 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
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
Published2020
Admission routes1
Has abstractyes

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