Contemporary status, distribution, and trends of mixedwoods in the northern United States
Bibliographic record
Abstract
As interest in managing and maintaining mixedwood forests in the northern United States (US) grows, so does the importance of understanding their abundance and distribution. We analyzed Forest Inventory and Analysis data for insights into mixedwood forests spanning 24 northern US states from Maine south to Maryland and westward to Kansas and North Dakota. Mixedwoods, i.e., forests with both hardwoods and softwoods present but neither exceeding 75%–80% of composition, comprise more than 19 million hectares and more than one-quarter of the northern US forest. They are most common in the Adirondack – New England, Laurentian, and Northeast ecological provinces but also occur elsewhere in hardwood-dominated ecological provinces. These mixtures are common even within forest types nominally categorized as either hardwood or softwood. The most common hardwoods within those mixtures were species of Quercus and Acer, and the most common softwoods were species of Pinus, Tsuga, and Juniperus. Although mixedwoods exhibited stability in total area during our analysis period, hardwood saplings were prominent, suggesting widespread potential for eventual shifts to hardwood dominance in the absence of disturbances that favor regeneration of the softwood component. Our analyses suggest that while most mixedwood plots remained mixedwoods, harvesting commonly shifts mixedwoods to either hardwood- or softwood-dominated cover types, but more specific information is needed to understand the causes of these shifts.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".