Short-term Change in Forest Metrics at Grand Portage National Monument, Minnesota
Bibliographic record
Abstract
Forest health monitoring programs can provide routine feedback of key indices and periodic updates of ecosystem health. A forest monitoring program was initiated at Grand Portage National Monument in 2007 with plots resampled in 2014. Grand Portage National Monument is within the southern boreal forest and the suitable habitat for most of its common overstorey species is expected to shift northward and out of the park as climate change progresses. We assessed short-term change in forest health on 20 plots by specifically evaluating change in overstorey density and basal area, forest community composition, and White-tailed deer (Odocoileus virginianus) browsing impacts on the herbaceous layer. Pooled across all overstorey species, both density and basal area increased between sampling events, but neither differed among habitats. For individual species of interest, responses were varied, although balsam Fir (Abies balsamea) increased in both density and basal area over the seven years. The frequency of quadrats supporting at least one preferred browse species did not differ between sampling years although this was greater in upland plots (81%) compared with wet mesic sites (66%). The effect of sampling year on species’ richness depended on habitat. In 2007, richness was higher in upland plots, although in 2014, richness was higher in wet mesic plots. Pooled across both sampling years, modified floristic quality index was greater in wet mesic plots. Our work demonstrates the increasing dominance of A. balsamea at Grand Portage National Monument and that notable differences in forest metrics can be observed over relatively short times.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".