Urbanization Impacts Dissolved Organic Matter Concentration and Quality in a Southeastern United States Watershed
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Blackwater rivers are named due to their exceptionally high concentrations of chromophoric dissolved organic matter (CDOM). They are the predominant lotic ecosystem in the United States Southeastern Coastal Plain, a region experiencing some of the nation’s highest rates of development. This study assessed variability in DOM concentration and composition across forested to urbanized blackwater systems in coastal South Carolina, U.S. Dissolved organic carbon and nutrient concentrations as well as absorbance and fluorescence optical properties reveal that urban sites have lower concentrations, elemental ratios, and less complex DOM. In contrast, forested blackwater sites have concentrations an order of magnitude higher, elevated elemental ratios, and molecular size dominated by refractory terrestrial-like DOM. Urban blackwater rivers were observed to have DOM concentrations and composition more similar to brown water systems than rural blackwater systems. These findings suggest that the urbanization of blackwater ecosystems results in lower concentrations and the export of simpler, more labile DOM, potentially lowering dissolved oxygen concentrations, increasing atmospheric carbon emissions and other negative impacts. To protect blackwater systems, baseline DOM concentrations and composition must be established to decipher impacts on water quality due to naturally occurring versus anthropogenic activities and to properly assign classifications to these diverse systems across the U.S.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".