Solute evidence for hydrological connectivity of geographically isolated wetlands
Bibliographic record
Abstract
Abstract Hydrological connectivity describes the water‐mediated transfer of mass, energy, and organisms between landscape elements and is the foundation for understanding how individual elements such as wetlands and streams integrate to support ecosystem services and nature‐based solutions in the landscape. Hydrological connectivity of geographically isolated wetlands (GIWs)—that is, wetlands without persistent surface water connections—is particularly poorly understood. To better understand GIW hydrological connectivity, we use a novel chloride mass‐balance approach to quantify the local runoff generation (defined as precipitation minus evapotranspiration, assuming negligible long‐term water storage) for 260 GIW subcatchments across North America. To evaluate hydrological connectivity, we compare the estimated local runoff from GIW subcatchments with the catchment‐average runoff. These comparisons provide three novel insights regarding the magnitude and variability of GIW hydrological connectivity. First, across 10 study regions, GIW subcatchments generate runoff at 120% of the mean catchment rate, implying they are well‐connected elements of the larger hydrologic landscape. Second, there is substantial heterogeneity in runoff generation among GIW subcatchments, which may enable support for a wide array of ecosystem functions and services. Finally, observed heterogeneity in runoff generation was largely uncorrelated to simple linear geographic predictors, indicating that GIW landscape position cannot reliably predict hydrological connectivity. In stark contrast to a priori legal assumptions that GIWs exhibit low or no hydrological connectivity, our results suggest that GIW subcatchments are active landscape features in runoff generation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".