AI & Environment Resource HubInstitute of the Americas · 2025-05-01
Description not yet supplied.
Intended audience has not been documented.
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Institute of the Americas. 2025-05-01. 20,000 Kilometers of Code: AI's Secret Voyage Across The Ocean. https://iamericas.org/20000-kilometers-of-code-ais-secret-voyage-across-the-ocean/ (AI & Environment Resource Hub; record report-168; collection snapshot 2026-10-04).
Included in the original Policy Document collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Policy Document · Row 172 · Original ID: report-168.
These are policy resources, not a verified register of enacted laws. Legal status and jurisdiction must be checked at source. Source blocks automated access · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.
Suggest a correction for this record →Publication, placement, and evidence are tracked separately. A legacy coverage flag alone does not prove an appearance.
Exact source link in saved original issue resource section: Policy Documents 🏛️
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Water; Oceans and coasts
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Shared topics: Water; Oceans and coasts
A machine learning framework was developed to efficiently parametrize large-scale hydrogeologic models by learning the relationship between water table depth and hydraulic conductivity from a small number of simulations, enabling realistic predictions with significantly reduced computational cost.
Shared topics: Water; Oceans and coasts
This study introduces a new foundation model based on the Prithvi-EO Vision Transformer, pre-trained on Sentinel-3 ocean color data, to enhance marine Earth observation. By fine-tuning on chlorophyll and primary production tasks, the model outperforms traditional baselines, showing that self-trained AI can extract detailed spatial patterns from limited labeled data and improve monitoring of ocean ecosystems and climate processes.
Shared topics: Water; Oceans and coasts
ABALOBI – meaning fisher in the isiXhosa language – is a South African-based organisation with global reach. Through our world-first fisheries improvement technologies and programmes, we strive to poise small-scale fisheries for ecological, economic and social sustainability.