Multifactor classification of forest landscape ecosystems of Jocassee Gorges, southern Appalachian Mountains, South Carolina
Bibliographic record
Abstract
Ecosystem classification identifies interrelationships within and among the geomorphology, soils, and vegetation that converge to form ecosystems across forest landscapes. We developed a multifactor ecosystem classification system for a 13 000 ha southern Appalachian landscape acquired in 1998 by the South Carolina Department of Natural Resources. Using a combination of multivariate analyses, we distinguished five ecosystem types ranging from xeric oak (Quercus spp.) to mesic eastern hemlock (Tsuga canadensis (L.) Carrière) ecosystems. Ecosystems segregated along geomorphic gradients influencing potential moisture availability, with soil properties such as solum thickness distinguishing among ecosystems occupying similar topographic positions. Our results suggest that different combinations of geomorphic and soil factors interact to form similar ecosystems across the landscape, and a given environmental factor can impact ecosystem development at some constituent sites of an ecosystem type but not at other sites. A regional comparison of ecosystem classifications indicates that environmental variables important for distinguishing ecosystems in the southern Appalachians vary, with Jocassee Gorges characterized by unique suites of environmental complexes. Our study supports the contention that the strengths of ecosystem classification are providing (i) comprehensive information on the interrelationships among ecosystem components, (ii) a foundation from which to develop ecologically based forest management plans, and (iii) an ecological framework in which to conduct future research on specific ecosystem components or processes.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".