Can structural and functional characteristics be used to identify riparian zone width in southern Appalachian headwater catchments?
Bibliographic record
Abstract
We characterized structural and functional attributes along hillslope gradients in headwater catchments. We endeavored to identify parameters that described significant transitions along the hillslope. On each of four catchments, we installed eight 50 m transects perpendicular to the stream. Structural attributes included woody and herbaceous vegetation; woody debris and forest floor mass, nitrogen (N) and carbon (C); total soil C and N; litterfall amount and quality by species; and microclimatic conditions. Functional attributes included litter decomposition, soil microarthropods, soil CO2evolution, soil solution chemistry, and soil extractable N. Forest floor mass, N and C, and soil depth increased with distance from the stream and transitioned between 10 and 20 m. In contrast, litterfall N rate (kilograms of nitrogen per hectare per day), downed woody debris, soil A-horizon C and N, and soil solution NO3concentration all decreased with distance, and exhibited significant transitions. Certain overstory species were more abundant in the uplands than near the stream. Herbaceous diversity and richness were similar across the hillslope, but species distributions varied in response to hillslope moisture content. Taken together, these results suggest that at 10–20 m from the stream, transitions occur that separate riparian from upland conditions and may provide valuable insight into riparian zone definition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".