Seasonality of inundation in geographically isolated wetlands across the United States
Bibliographic record
Abstract
Inundation area is a major control on the ecosystem services provisioned by geographically isolated wetlands. Despite its importance, there has not been any comprehensive study to map out the seasonal inundation characteristics of geographically isolated wetlands over the continental United States (CONUS). This study fills the aforementioned gap by evaluating the seasonality or the long-term intra-annual variations of wetland inundation in ten wetlandscapes across the CONUS. We also assess the consistency of these intra-annual variations. Finally, we evaluate the extent to which the seasonality can be explained based on widely available hydrologic fluxes. Our findings highlight significant intra-annual variations of inundation within most wetlandscapes, with a standard deviation of the long-term averaged monthly inundation area ranging from 15% to 151% of its mean across the wetlandscapes. Stark differences in inundation seasonality are observed between snow-affected vs. rain-fed wetlandscapes. The former usually shows the maximum monthly inundation in April following spring snowmelt (SM), while the latter experiences the maximum in February. Although the magnitude of inundation fraction has changed over time in several wetlandscapes, the seasonality of these wetlands shows remarkable constancy. Overall, commonly available regional hydrologic fluxes (e.g. rainfall, SM, and evapotranspiration) are found to be able to explain the inundation seasonality at wetlandscape scale with determination coefficients greater than 0.57 in 7 out of 10 wetlandscapes. Our methodology and presented results may be used to map inundation seasonality and consequently account for its impact on wetland functions.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".