eScholarship
metadata only
Using automated machine learning for the upscaling of gross primary productivity
Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning#499211
- Declining Ecosystem Respiration Linked to Nitrogen Deposition: Insights From a 26-Year FLUXNET Record#120169
- Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings#1035604
- Seasonal Predictions and Their Applications in the Mediterranean Region: Part I—Sources of Predictability and Prediction Skill#137606
- Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics#443670
Record · ID 1145865
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive