Home › Entities › academia › Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality

Research article (London School of Economics and Political Science Research Online (London School of Economics and Political Science), 2021) · cited 29× · AI/ML
academia openalex:W4226519533
Press Enter · cited answer in seconds

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality

Summary

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality is a scholarly article[1].

Key Facts

  • Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality. Retrieved May 24, 2026, from https://4ort.xyz/entity/assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric
MLA “Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric.
BibTeX @misc{4ortxyz_assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality}}, year = {2026}, url = {https://4ort.xyz/entity/assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality — https://4ort.xyz/entity/assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/assessing-a-fossil-fuels-externality-with-a-new-neural-networks-and-image-optimisation-algorithm-the-case-of-atmospheric · Last refreshed: