Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests

Literals

ou:bibtex
- @article{Maya2023,title = {Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests},journal = {Science of the Total Environment},year = {2023},volume = {871},author = {Mills, S.A. and Bousiotis, D. and Maya-Manzano, J.M. and Tummon, F. and MacKenzie, A.R. and Pope, F.D.}}
dcterms:contributor
- Mills S.A., Bousiotis D., Maya-Manzano J.M., Tummon F., MacKenzie A.R., Pope F.D.
dcterms:publisher
- Science of the Total Environment
dcterms:creator
- Mills S.A.
bibo:volume
- 871
bibo:eissn
- 1879-1026
bibo:doi
- 10.1016/j.scitotenv.2023.161969
ou:eid
- 2-s2.0-85147842794
fabio:hasPublicationYear
- 2023
vivo:identifier
- 2023-583
bibo:issn
- 0048-9697
ou:tipoPublicacion
- Article
dcterms:title
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests
vcard:url
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147842794&origin=inward
ou:urlOrcid
- http://www.scopus.com/inward/record.url?eid=2-s2.0-85147842794&partnerID=MN8TOARS
ou:urlScopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147842794&origin=inward