EXAMINING ASPEN EXPANSION FROM BEFORE AND AFTER PRESCRIBED BURNING IN A NATIVE FESCUE GRASSLAND THROUGH GEOSPATIAL TECHNIQUES
Bibliographic record
Abstract
Native fescue (Fescue spp.) grasslands of the Intermountain West have become increasingly scarce due to the advent of modern agriculture, the loss of Indigenous people’s land management practices, modern wildfire management and the extirpation of bison (Bison bison bison). Native grassland is a biodiversity hot-spot, is significant for carbon sequestration, and essential to many species of flora and fauna that occur in the ecosystem. Our study site, on the Rocky Mountain Front in Waterton Lakes National Park, Alberta Canada, consists of 30 discrete aspen stands (Populous tremuloides) which are encroaching on this declining shortgrass fescue grassland. Parks Canada is attempting to suppress aspen expansion and improve fescue prairie through ecological restoration by instituting prescribed burns and elk (Cervus elaphus) browse. Prescribed burns will decrease woody vegetation through adult aspen stem mortality while stimulating regeneration, which is subsequently browsed by elk. The park has a wolf pack (Canis lupus) that preys primarily on the elk, thereby affecting aspen stem recruitment spatially. These dynamics create a natural laboratory for examining the interaction of fire, elk and wolves that impact the aspen/grassland dynamics. We measured the aspen stand structure before and after a prescribed burn set in spring of 2017 to determine the change in aspen stand area from before to after the burn. We measured aspen stands before the prescribed burn during the summer of 2016 via GNSS handheld mapping units. We collected post-burn measurements in summer 2017 via unmanned aerial system (UAS). We also conducted ground measurements for a subset of aspen stands in 2017 to ground-truth the aerial photography data. We used knowledge Engineer (KE) in Erdas Imagine for classifying the UAS imagery and then created polygons in ArcGIS to analyze the data from before and after prescribed burning. We also digitized all aspen stand layers from the UAS imagery through the heads-up digitization technique and used these data to compare the aspen stands from before to after prescribed burning. Aspen stand area did not decline at a statistically significant level for any layers we measured: canopy, regeneration, and shrub expansion before and after prescribed burning. We did see an observational decline in the total aspen canopy area.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".