An Ecohydrologically-informed Machine Learning Approach for Understanding Dryland Carbon Dynamics
Bibliographic record
Abstract
Drylands have tightly coupled water and carbon cycles due to persistent water scarcity, making them valuable systems for understanding coupled ecohydrological and biogeochemical processes. In addition, dryland ecosystems contribute significantly to the interannual variability of the terrestrial carbon sink. To better characterize dryland carbon dynamics, we present DryFlux, a machine learning upscaled product based on a dense network of eddy covariance sites in the North American Southwest. This product combines in-situ fluxes with remote sensing and meteorological data to estimate gross primary productivity in drylands while explicitly accounting for water limitation during the model development process. DryFlux outperforms existing products in capturing interannual and seasonal variation in carbon uptake when used globally. We specifically explore how machine learning techniques can accurately upscale fluxes at multiple spatial (1 km and 9 km) and temporal (daily, weekly, monthly) scales to find the best resolution for capturing spatial and temporal heterogeneity in carbon and water fluxes. In addition, we discuss how remotely sensed soil moisture from satellites can help capture biogeochemical 'hot spots' and 'hot moments' in drylands. Our findings can help us better understand dynamic carbon fluxes in drylands, as well as the spatiotemporal resolution needed to resolve water-carbon dynamics in these and other systems. Machine learning methods that explicitly incorporate water limitation in model development can contribute to a more comprehensive understanding of carbon, energy, and water fluxes at multiple scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".