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Record W6901657575 · doi:10.6068/dp172af094e9218

TREND: European Centre for Disease Prevention and Control. Coronavirus Disease 2019 (COVID-19): Total Confirmed Coronavirus Disease 2019 (COVID-19) Cases | Country: Canada, 12/31/2019 - 06/12/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 092-001-003

2020· other· en· W6901657575 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirusDiseasePublic healthEpidemiologyPandemicMiddle East respiratory syndromeDisease preventionDisease control

Abstract

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European Centre for Disease Prevention and Control. Coronavirus Disease 2019 (COVID-19): Total Confirmed Coronavirus Disease 2019 (COVID-19) Cases | Country: Canada, 12/31/2019 - 06/12/2020. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 092-001-003 Dataset: Reports a cumulative total of reported cases confirmed as coronavirus disease 2019 (COVID-19) worldwide by nation. ECDC defines confirmed cases as a person with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms. Data Planet calculates the cumulative total of casesas a sum of daily cases reported by ECDC since 12/31/2019. At the end of December 2019, Chinese public health authorities reported several cases of acute respiratory syndrome in Wuhan City, Hubei province, China. Chinese scientists soon identified a novel coronavirus as the main causative agent. The disease is now referred to as coronavirus disease 2019 (COVID-19), and the causative virus is called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses are viruses that circulate among animals with some of them also known to infect humans. Bats are considered natural hosts of these viruses though several other species of animals are also known to act as sources. Zoonotic coronaviruses have emerged in recent years to cause human outbreaks, such as the Severe Acute Respiratory Syndrome (SARS) in 2003 and the Middle East Respiratory Syndrome (MERS) since 2012. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide Category: Health and Vital Statistics Subject: Epidemics, Epidemiology, Diseases Source: European Centre for Disease Prevention and Control An agency of the European Union, the European Centre for Disease Prevention and Control (ECDC) was established in 2005. Its mission is to identify, assess and communicate current and emerging threats to human health posed by infectious diseases. In order to achieve this mission, ECDC works in partnership with national health protection bodies across Europe to strengthen and develop continent-wide disease surveillance and early warning systems. https://www.ecdc.europa.eu/en

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)0.003
metaresearch head score (Gemma)0.024
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 score0.540
Threshold uncertainty score0.916

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.013
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0040.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1530.084

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 head0.065
GPT teacher head0.326
Teacher spread0.261 · 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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