Presettlement forests and fire in southern Alabama
Bibliographic record
Abstract
Although the loss of Pinus palustris P. Mill. (longleaf pine) dominated communities and the alteration of the presettlement fire regimes have been documented, there is little information on the ecology of the presettlement lower Coastal Plain forests of the southeastern United States. We used 12 637 witness trees, which were recorded by General Land Office surveyors between 1820 and 1846, to identify presettlement witness tree associations and to explore witness tree – environmental variable relationships. Detrended correspondence analysis (DCA) was used to identify three witness tree associations including a Pinus spp. – Quercus marilandica (L.) Muenchh. association, a Quercus spp. – Carya spp. association, and a Persea spp. – Fagus grandifolia Ehrh. association. Canonical correspondence analysis and contingency tables were used to describe and test witness tree relationships with slope, elevation, and soil drainage. Additionally, bearing distances, used as an indicator of forest density, were compared among the witness tree associations. Species orientation shown by the DCA ordination diagram was interpreted as a gradient of fire frequency. This interpretation of a fire gradient was supported through the analysis of bearing distances, which showed high bearing distances associated with witness trees located on the high fire frequency end of the gradient. The relationships between witness trees and environmental variables as well as relationships between witness trees and bearing distances suggest that fire-dependent longleaf communities dominated the presettlement study area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".