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Record W3090577155 · doi:10.5539/ep.v9n2p19

Global Environmental Pollution and Coronavirus

2020· article· en· W3090577155 on OpenAlexvenueno aff
Masrur Abdul Quader

Bibliographic record

VenueEnvironment and Pollution · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirusCase fatality ratePandemicCoronavirus disease 2019 (COVID-19)Mortality rateSevere acute respiratory syndrome coronavirusNatural deathEnvironmental healthPollutionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyGeographyMedicineBiologyDemographyMedical emergencyEcologyPopulationInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

Discussions, analyses and modelling are based on global level data. The natural environment is causing deaths to its habitants. The ongoing coronavirus is also doing damage to the lives of the glove. Worldwide people are too much worried putting extra ordinary efforts to contain the coronavirus pandemic. But the damage being done to the lives of the people on the glove by natural environment problems is substantially higher than that done by the coronavirus. Air pollution death rate is 6.02 times of death rate due to coronavirus and the total environmental death rate is 10.85 times that of coronavirus death rate. Three statistical models regarding coronavirus development, coronavirus spread and coronavirus fatality are developed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.346
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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