Assessment of residual slash coverage using UAVs and implications for aspen regeneration
Bibliographic record
Abstract
Proper redistribution of residual slash following harvesting is crucial for ensuring successful regeneration and continued health in trembling aspen (Populus tremuloides) forests. As traditional methods of measuring residual slash are a strenuous and tedious process, the objective of this study was to develop a new, faster, and more detailed method to assess residual slash distribution for entire harvested blocks. This study also aimed to assess the influence residual slash coverage had on the success of aspen regeneration 1 year after winter harvesting. Using high-resolution UAV imagery and maximum likelihood supervised image classification, residual slash was differentiated from the underlying forest floor. Overall, classification accuracy ranged between 85% and 96% with the highest accuracy occurring when aerial imagery was collected at the beginning of the second spring following winter harvesting. Slash distribution was quite consistent across harvested blocks, with 92% of harvested blocks experiencing <33% coverage. There was no relationship between the level of aspen regeneration following 1 year of growth and percentage slash coverage up to 60%. No vegetation plots occurred in areas with >60% slash coverage; therefore, it is unknown whether aspen regeneration will be affected in areas with higher slash coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".