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Pandemocene: a review

2023· review· en· W4390457048 on OpenAlexaboutno aff
Cem Turaman

Bibliographic record

VenueInternational Journal of Community Medicine and Public Health · 2023
Typereview
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicEpoch (astronomy)OutbreakQuarter (Canadian coin)PopulationInfectious disease (medical specialty)DemographyGeographyDiseaseCoronavirus disease 2019 (COVID-19)HistoryBiologyMedicineVirologyComputer scienceArchaeologySociology

Abstract

fetched live from OpenAlex

The history of humans has been divided into epochs, which are characterized by the prevalence of comparable conditions and are named based on their direct or indirect interactions with their environments. It is widely recognized that we are currently in the Anthropecene epoch. Infectious diseases have impacted humans in a different extent throughout its history. The numbers of viral infection pandemics in three quarter-century clusters (25 years each) formed from 1950 to the present day were compared by examining data figures from the literature. The source of the data is obtained entirely from the published articles relating the subject. The frequency of infectious disease pandemics has increased significantly in recent decades. Roughly speaking, there is an interaction between human population density, human activities and the impact of infection outbreaks. This increase in the frequency of pandemics may be associated with the increase in population density as well as the negative impact of humans on the environment. It may be appropriate to coin this most recent epoch as Pandemocene.

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)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.844
GPT teacher head0.643
Teacher spread0.201 · 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
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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