Forest disturbance frequency and patch structure from pre-European settlement to present in the Mixed Forest Province of Minnesota, USA
Bibliographic record
Abstract
We used General Land Office survey data (1860–1890) and interpreted aerial photography from the 1930s, 1970s, and 1990s to quantify forest disturbance frequency and spatial patterns for four time periods in the Mixed Forest Province of Minnesota. The study region included eight subsections within the Mixed Forest Province of Minnesota’s Ecological Classification System. Presettlement disturbance and spatial pattern estimates varied across the eight subsections indicating a strong relationship to soil and landform characteristics. Land surveyors primarily recorded higher severity disturbances that resulted in significant tree mortality. The 1900–1940 era was characterized by a short-term increase in fire frequency that was relatively uniform across the study region, in contrast to the variability of the presettlement (1860–1890) landscape. In the postsettlement period (1940–1995), timber harvest replaced fire as the dominant disturbance factor. Similar management practices among subsections created similar harvest rates throughout the study region. These management practices imposed a more homogeneous pattern dominated by small (10–25 ha) patches. Management practices now have a greater influence than natural processes in the generation of landscape pattern in the Mixed Forest Province of Minnesota. Information on presettlement forest conditions and subsequent changes can be used by land managers to restore spatial pattern variability in managed forest landscapes.
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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".