aceA random forest model trained on 16S rRNA microbial community data from a field mesocosm predicted antifouling paint particle presence in sediment, with perfect detection of presence in test set and correct classification of all uncontaminated and 3 of 5 contaminated real-world Baltic Sea sites.
On 2026-08-03, a peer-reviewed study reported a random forest model trained on 16S rRNA gene sequencing from a field mesocosm to predict antifouling paint particle contamination in sediment. In lab incubation tests it identified 100% of presence samples and 83.3% of absence samples, and when applied to 14 Baltic Sea and Warnow estuary sites it correctly labeled all uncontaminated sites and three of five contaminated sites.
- Impact 30%
- 49
- Evidence 25%
- 95
- Scale 20%
- 35
- Confidence 15%
- 87
- Recency 10%
- 86
Updated Aug 4, 2026 · TRV-2026-0643
67Index score