Quantitative classification of a historic northern Wisconsin (U.S.A.) landscape: mapping forests at regional scales
Bibliographic record
Abstract
We developed a quantitative and replicable classification system to improve understanding of historical composition and structure within northern Wisconsin's forests. The classification system was based on statistical cluster analysis and two forest metrics, relative dominance (% basal area) and relative importance (mean of relative dominance and relative density), as computed from the original U.S. Public Land Survey (PLS) bearing-tree data. Broad forest patterns are consistent between the two metrics; yet, detailed inspection highlights different aspects of historical structure. Maps produced characterize vegetation at regional scales and reveal patterns that can be interpreted in the context of environmental constraints. Our classifications have a fairly coarse spatial grain (2.6 km2) and fine-scale, patchily distributed ecosystems types are not represented. This resolution, however, is consistent with that of the PLS bearing-tree data, and maintaining it allowed retention of other beneficial map qualities, including quantitative representation of the data, replicability, flexibility, and an assessment of robustness and confidence. Our classifications are broadly applicable for regional-scale scientific and forest-management uses, including (i) assessing natural variability, (ii) determining the potential distribution of species, (iii) setting goals for ecological restoration, and (iv) calculating landscape change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".