Monitoring Root-Zone Soil Hydrodynamic Processes in Alpine Ecosystems Using Cosmic-Ray Neutron Sensing
Bibliographic record
Abstract
To evaluate root-zone soil water monitoring capability, CRNS stations were deployed across alpine meadow and shrubland on the eastern Tibetan Plateau. The hectometer-scale footprint (200-250 m radius) captured spatiotemporal heterogeneity that traditional TDR and FDR probes could not resolve. Time series analysis during snowmelt showed that CRNS-derived water storage increased by 38-52 mm over 20 days, aligning with lysimeter records. In shrubland plots, CRNS detected root-zone depletion at a rate of 2.3 mm•day⁻¹, significantly faster than meadow sites (1.5 mm•day⁻¹), reflecting vegetation-soil interactions. Coupling CRNS with a root-zone water balance model reduced estimation error in evapotranspiration by 18% (NSE from 0.72 to 0.85). This demonstrates CRNS as a powerful tool for ecohydrological studies in alpine ecosystems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".