Solute evidence for hydrological connectivity of geographically isolated wetlands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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