Physical and historical determinants of the pre- and post-settlement forests of northwestern Pennsylvania
Bibliographic record
Abstract
Our analysis of the early land survey records and more recent U.S. Forest Service inventory data documents the changing nature of northwestern Pennsylvania's forests following European settlement. Initially, the northern portion of the four-county study area was dominated by forests of Fagus grandifolia Ehrh. and Acer saccharum Marsh. associated with the richer, finer-textured soils of the rolling Glaciated Appalachian Plateau. Up to 80% of the region was cleared for farming in the 19th century. Marginal farmland was abandoned and reverted to forests in the 20th century. Fires and leached, nutrient-poor soils favored the dominance of Quercus spp. and Castanea dentata (Marsh.) Borkh. in the presettlement forests of the rugged Unglaciated Appalachian Plateaus to the south. The rough nature of the terrain discouraged the early clearance of the plateaus' forests. The advent of the petroleum industry and its insatiable demand for barrels in the 19th century, however, assured the selective removal of the larger (>20 in. (50 cm) DBH) Quercus alba L. from the region's woods. The increasing homogeneity of northwestern Pennsylvania's forests today is due to the sharp decline of the more distinctive indicator species and the rise of a number of opportunistic old-field or gap species, notably Prunus serotina Ehrh. and Acer rubrum L.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".