National hydrologic connectivity classification links wetlands with stream water quality
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
Wetland hydrologic connections to downstream waters influence stream water quality. However, no systematic approach for characterizing this connectivity exists. Here using physical principles, we categorized conterminous US freshwater wetlands into four hydrologic connectivity classes based on stream contact and flowpath depth to the nearest stream: riparian, non-riparian shallow, non-riparian mid-depth and non-riparian deep. These classes were heterogeneously distributed over the conterminous United States; for example, riparian dominated the south-eastern and Gulf coasts, while non-riparian deep dominated the Upper Midwest and High Plains. Analysis of a national stream dataset indicated acidification and organic matter brownification increased with connectivity. Eutrophication and sedimentation decreased with wetland area but did not respond to connectivity. This classification advances our mechanistic understanding of wetland influences on water quality nationally and could be applied globally.
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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 | 0.001 | 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) | 0.001 | 0.002 |
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