Native American influences on the forest composition of the Allegheny Plateau, northwest Pennsylvania
Bibliographic record
Abstract
We integrate witness tree distribution, Native American archaeological sites, and geological and topographic variables to investigate the relationships between Native American populations and pre-European settlement forest types on the Allegheny Plateau, northwest Pennsylvania. Detrended correspondence analysis of witness tree data separated the presettlement forests into oakhickorychestnut and beechhemlockmaple communities. Oak, hickory, and chestnut forests were centered on Native American village sites. Using archaeological data, an index of Native American influence (NAI) was derived to reflect the intensity of Native American land use across the landscape. In a comparison among species, mean NAI value of oak, hickory, and chestnut trees was significantly higher than that of beech, maple, and hemlock. Logistic regression demonstrated that among geology type, landform, elevation, aspect, slope, and NAI, NAI was by far the most significant predictor of oak, hickory, and chestnut distribution. Although cause and effect of this relationship cannot be tested, we suggest that long-term Native American activity selected for the disturbance-adapted oak, hickory, and chestnut. We contend that Native American agriculture, burning, and resource extraction could have converted maplebeechhemlock to oakhickorychestnut, or at least reinforced the dominance of this forest type.
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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".