Effects of Watershed Impervious Cover on Dissolved Silica Loading in Storm Flow<sup>1</sup>
Bibliographic record
Abstract
Abstract: Dissolved silica (DSi) availability is a factor that affects the composition of algal populations in aquatic ecosystems. DSi cycling is tightly linked to the hydrological cycle, which is affected by human alterations of the landscape. Development activities that increase impervious cover change watershed hydrology and may increase the discharge of DSi‐poor rainwater and decrease the discharge of DSi‐rich ground water into aquatic ecosystems, possibly shifting algal community composition toward less desirable assemblages. In this study, DSi loadings from two adjacent coastal watersheds with different percent impervious cover were compared during four rain and five nonrain events. Loadings in the more impervious watershed contained a significantly larger proportion of surface runoff than base flow (ground‐water discharge) and had lower [DSi] water during rain events than the less impervious watershed. Application of the Soil Conservation Service Curve Number (CN) method showed that the minimum rainfall height necessary to yield runoff was significantly lower for the more impervious watershed, implying that runoff volumes increase with impervious cover as well as the frequency of runoff‐yielding events. Empirical data collected during this study and estimates derived from the CN method suggest that impervious cover may be responsible for both short‐term DSi limitation during rain events as well as long‐term reduction of DSi inputs into aquatic ecosystems.
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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 | 0.001 |
| 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 | 0.000 |
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".