Eco-hydrologic model for assessing the climate and hydrologic elasticity of vegetation in mountain wetlands
Bibliographic record
Abstract
Abstract Climate sensitive mountain wetlands are complex eco-hydrologic interactions that make their future behavior difficult to predict, and the lack of long-term systematic observations compounds this difficulty. To overcome these limitations, this study developed an eco-hydrologic model that reflects only the essential eco-hydrologic processes in mountain wetlands, and explored the hydrologic response of mountain wetlands to the presence or absence of vegetation and the elasticity of mountain wetland’s vegetation to climate and hydrologic variables. Based on an eco-hydrologic model customized for the Janggun wetland and Hwaeom marsh in the southeastern Korea, this study derived elasticity curve across multiple percentiles and found that the climate elasticity of hydrologic components varied significantly with the presence or absence of vegetation. In particular, vegetation was more sensitive to maximum temperature than precipitation, and the effect of temperature was greater during the vegetation die-off period. In addition, the vegetation in Janggun wetland and Hwaeom marsh exhibited distinct sensitivities responding more strongly to soil moisture and groundwater exchange rate, respectively, suggest that the hydro-geomorphologic characteristics of mountain wetlands may play a critical role in determining how climate change affects wetland vegetation. The eco-hydrologic model, including the vegetation module, is expected to serve as a basic tool for climate change adaptation strategies in mountain wetlands in the future.
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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 | 0.001 |
| 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) | 0.001 | 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".