Spatial distribution of forest landscape change in western New York from presettlement to the present
Bibliographic record
Abstract
Changes in tree taxon composition and distribution in western New York over a 200 year time period ca. 1797–1993 were examined by comparing the presettlement land survey with the US Forest Inventory and Analysis (FIA) survey. To ensure data quality, biases in presettlement bearing tree selection and FIA plot location were assessed. A 6 mile × 6 mile grid of taxa abundance was then estimated using geostatistics. Overall, significant changes in taxon composition occurred, with the taxa most abundant in the presettlement land survey — beech (37.0%), sugar maple (21.0%), and eastern hemlock (8.3%) — being replaced by sugar maple (19.2%), ash (11.7%), and red maple (11.4%) in the FIA survey. Spatially resolved comparisons showed that the landscape changed from fairly spatially homogeneous to more heterogeneous; in the presettlement survey, the most abundant taxon in most of the 6 mile × 6 mile grid cells was beech, while in the present survey, the most abundant taxon in most of the cells was one of several early successional taxa that each displayed a distinctly clustered geographic pattern of dominance. The clusters of dominance of the different early successional taxa may correspond to environmental factors. This study demonstrates the insights available through spatially resolved analyses of changes in the forest landscape between presettlement and present.
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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 | 0.000 | 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) | 0.001 | 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".