A comparison of presettlement and modern forest composition along an elevation gradient in central New Hampshire
Bibliographic record
Abstract
Tree species composition is influenced not only by edaphic and climatic factors but also by natural and human-caused disturbances. To understand interactions among these influences, we compared forest species composition data from the time of European settlement with modern data. We derived elevation data for 2529 trees mapped by early land surveys (1770–1850) across a 1000 m elevation gradient in central New Hampshire and compared these with modern data (2004–2009) from the Forest Inventory and Analysis program (123 plots containing 2126 trees) and from permanent plots representing case studies of different land-use histories. Spruce and beech are much less abundant today at all elevations than they were prior to settlement, while maples and birches have increased. Fir, hemlock, pines, and oaks have changed little in distribution, although pines and oaks increased in abundance somewhat. Land-use history (agriculture below 500 m and cutting of various intensities at all elevations) is likely the primary explanation for these shifts, although climate change is also an important factor for some. A clearer understanding of presettlement forest composition improves our ability to separate the relative importance of natural and human-driven influences on the species composition of today’s forests.
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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 | 0.001 | 0.001 |
| 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".