Interception of Rainfall in Successional Tropical Dry Forests in Brazil and Costa Rica
Why this work is in the frame
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Bibliographic record
Abstract
Tropical dry forests (TDF) are endangered ecosystems characterized by a matrix of successional forest patches with structural differences across the Neotropics. Until now, there have been few studies that analyze the partitioning of rainfall by forest interception in TDF. To contribute to the understanding of the TDF impact on the hydrological dynamic at the ecosystem and landscape levels, a rainfall interception study was conducted in Santa Rosa National Park in Costa Rica (SRNP) and in Mata Seca State Park in Brazil (MSSP). In each site, three plots per successional stage were studied. The successional stages were early, intermediate, and late. In each plot the rainfall, throughfall, and stemflow were monitored during one rainy season. The relationship between gross rainfall and water fluxes was evaluated using linear regression models. In general, net rainfall oscillated from 79.3% to 85.4% of gross rainfall in all the plots in MSSP without any trend related to forest succession, due to the effect of a high density of lianas in the intermediate and late stage plots. In SRNP, there was a clear trend of net rainfall among successional stages: 87.5% (early), 73.0% (intermediate), and 63.4% (late). Net rainfall correlated negatively only with plant area index in SRNP (r = −0.755, p < 0.05). This study highlights the need to study rainfall interception in successional stages to estimate net rainfall that reaches the soil. This would provide better hydrological information to understand water balance and water fluxes at the level of forest ecosystems and landscapes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it