# Collaborative Research: CAIG: Assemblage-based machine learning to quantify oceanic geochemical fluxes from marine particle images

**Budget Signal Score:** 40/100

| Field | Value |
|---|---|
| Institution | University of Rhode Island |
| Location | KINGSTON, RI |
| Project | Collaborative Research: CAIG: Assemblage-based machine learning to quantify oceanic geochemical fluxes from marine particle images |
| Award amount | $332,242 |
| Obligated so far | $332,242 |
| Awarded | 2026-08-03 |
| Period | 2027-01-01 to 2029-12-31 |
| Program | AI and Geosciences |
| Directorate / division | GEO / GCP |
| Award ID | 2615517 |

## Why this score
- Award of $300K or more.

Source: U.S. National Science Foundation Awards API (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2615517). Last refreshed: 2026-11-03T18:10:42.804Z.
Award data comes from the NSF Awards API and may be amended by NSF. Institution-level only; investigator contact details are not included. Informational only.