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Record W4310876000 · doi:10.1039/d2ma00797e

COVID-19 mitigation: nanotechnological intervention, perspective, and future scope

2022· article· en· W4310876000 on OpenAlexaff
Arpita Adhikari, Dibyakanti Mandal, Dipak Rana, Jyotishka Nath, Aparajita Bose, Sonika Sonika, Jonathan Tersur Orasugh, S. S. De, Dipankar Chattopadhyay

Bibliographic record

VenueMaterials Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Ottawa
FundersUniversity of CalcuttaDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsCoronavirus disease 2019 (COVID-19)GlobeScope (computer science)2019-20 coronavirus outbreakPerspective (graphical)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicIntervention (counseling)Intensive care medicineMedicineDevelopment economicsPolitical scienceEnvironmental healthVirologyEconomicsPsychiatryComputer sciencePathologyDiseaseOutbreak

Abstract

fetched live from OpenAlex

COVID-19 infections and severe acute respiratory syndrome (SARS) have caused an unprecedented health crisis across the globe with numerous deaths, as well as causing a tremendous economic crash worldwide.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.999

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)0.0020.000

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 head0.021
GPT teacher head0.327
Teacher spread0.307 · 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 designBench or experimental
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

Citations11
Published2022
Admission routes1
Has abstractyes

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