MétaCan
Menu
Back to cohort
Record W3014311489 · doi:10.9734/arrb/2020/v35i130182

Coronavirus Outbreak and the Mathematical Growth Map of COVID-19

2020· article· en· W3014311489 on OpenAlexaff
Md. Kamrujjaman, Md. Shahriar Mahmud, Md Shafiqul Islam

Bibliographic record

VenueAnnual Research & Review in Biology · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of Prince Edward IslandUniversity of Calgary
Fundersnot available
KeywordsOutbreakCoronavirus disease 2019 (COVID-19)CoronavirusPopulationVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirusGeographyBiologyMedicineEnvironmental healthDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

In the last two decades the world had faced three respiratory syndrome outbreaks incurred by Coronavirus. Though the wild animals are the primary carriers of the virus, the human population managed to survive sacrificing more than 1,600 lives from 2002 to 2012. But the current virus outbreak has already taken more than 2,462 lives since 22 February 2020. In the first few days, when the cases were being introduced under light, there were no treatment for the infection and the unleashed spread demands to be analyzed to see the pattern of the outbreak. This manuscript aims to look into the growth map of the COVID-19 outbreak under mathematical growth functions and tries to understand which growth pattern assembles the scenario for the cases.

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)0.012
metaresearch head score (Gemma)0.166
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.166
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.643
GPT teacher head0.596
Teacher spread0.047 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations41
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Research & Review in BiologySame topicCOVID-19 epidemiological studiesFrench-language works237,207