Management impacts on <i>Carex assiniboinensis and Cardamine concatenata</i> on the Ottawa National Forest
Bibliographic record
Abstract
Monitoring of herbaceous plants on the Ottawa National Forest (ONF) is used to understand the impact of forest management on understory composition and site conditions. In their planning, national forests are required to take into account management impacts on diversity and ecosystem health. The effect of management on understory species is dependent on various factors, including the intensity of disturbance and the biology of the plant. In the first study in this report, a population of Carex assiniboinensis, a Michigan state threatened species, was monitored for seven seasons including before logging commenced, in order to determine the sedge’s response to a single-tree selection harvest. Analyses provided insights for management of C. assiniboinensis at the stand level over the short-term. In the second study in this report, the use of the cutleaf toothwort (Cardamine concatenata) as a Management Indicator Species on the ONF was reviewed. Data were analyzed to determine the suitability of using C. concatenata to monitor impacts of forest management on site conditions. The various factors that affect understory species population dynamics illuminated the challenges of using indicator species to monitor site conditions. Insights from the study provide a greater understanding of management impacts on understory species across the Ottawa National Forest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".