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Record W4205348222 · doi:10.31038/idt.2021212

Cometary Origin of COVID-19

2021· article· en· W4205348222 on OpenAlexaff
Edward J. Steele, Reginald M. Gorczynski, Herbert Rebhan, P. R. Carnegie, Robert Temple, Gensuke Tokoro, A.S. Kondakov, Stephen J. Coulson, D. T. Wickramasinghe, N. C. Wickramasinghe, Chandra Wickramasinghe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsHaplotypeOutbreakBiologyGenomeGeneticsPandemicCoronavirus disease 2019 (COVID-19)EpizooticVirologyMutationEvolutionary biologyGenotypeDiseaseGeneMedicine

Abstract

fetched live from OpenAlex

When analysed in patients at epicentres of outbreaks over the first three months of the 2020 pandemic, the virus responsible for COVID-19 cannot be classed as a rapidly mutating virus.It employs a haplotype-switching strategy most likely driven by APOBEC and ADAR cytosine and adenosine deamination events (C>U, A>I) at key selected sites in the ~ 30,000 nt positive sense single-stranded RNA genome (Steele and Lindley 2020).Quite early on (China, through Jan 2020) the main haplotype was L with a minor proportion of the S haplotype.By the time of the explosive outbreaks in New York City (mid-to late-March 2020) the haplotype variants expanded to at least 13.The COVID-19 genomes analysed at the main sites of exponential increases in cases and deaths over a 2 week time period (explosive epicentres) such as Wuhan and New York City showed limited mutation per se of the main haplotypes engaged in disease.When mutation was detected it was usually conservative in terms of significant alterations to protein structure.The coronavirus haplotypes whether in Wuhan, West Coast USA, Spain or New York differ by no more than 2-9 coordinated nucleotide changes and all genomes are thus ≥99.98% identical to each other.Further, we show that the most similar SARS-like CoV animal virus sequences (bats, pangolins) could not have caused the assumed zoonotic event setting off this explosive pandemic in Wuhan and regions: zoonotic causation via a Chinese wild bat SL-CoV reservoir jumping to humans by an intermediate amplifier (e.g.pangolins) is clearly not possible on the basis of the available data.We also discuss the evidence for airborne transmission of COVID-19 as the main infection route and highlight outbreaks on certain ships at sea consistent with their hypothesised cosmic origins.We conclude that the virus originated as a pure genetic strain in a life-bearing carbonaceous meteorite which was first deposited in the tropospheric jet stream over Wuhan.Over the next month or so this viral-laden dust cloud not only descended through the troposphere to target Wuhan and its environs, but was also transported in a Westerly direction through the mid-latitude northern jet stream causing explosive in-fall events sequentially over Iran, Italy, Spain and then New York City in the early months of the pandemic to the end of March 2020.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.098
GPT teacher head0.413
Teacher spread0.315 · 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 designTheoretical or conceptual
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

Citations4
Published2021
Admission routes1
Has abstractyes

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