Impact of browsing after burning on aspen growth and litter decomposition
Bibliographic record
Abstract
Manitoba has a small number of rough fescue (Festuca hallii) grasslands, which are commonly utilized by elk for winter grazing in upland Manitoba. Aspen encroachment currently threatens these grasslands within Riding Mountain National Park, where augmentation of grasslands is a priority. This study examined the interaction between browsing and fire on aspen growth, the effect of browser manure on aspen leaf decomposition, and examined soil profiles to identify historic grasslands. Browsing simulations were carried out within exclosures across sites with-and-without recent burns to measure the effect of browsing alone versus a combination of fire and browsing. To assess the effect of browsing on the apical shoot, a subset of trees was randomly assigned an apical shoot manual-removal. High intensity of clipping was effective to suppress aspen, but fire had no effect. Field-placed litter bags were used to measure the effect of ungulate manure on aspen litter decay. Soil fecal incubation were used to measure the release over two months of plant-available N as nitrate-N (NO3-N) and ammonium-N (NH4-N) in soil. The manure amendments used in both studies were bison and a wild-ungulate blend of deer, elk and moose. Aspen decomposition was seen at rates known for other studies, but manure was without impact. In addition, the use of soil organic carbon (SOC) at depth as an indicator for historic grasslands was tested. In grasslands, a large portion of the organic matter input is from fibrous root systems within the mineral soil. In forest, most of the residue input is from leaves falling to the litter layer above the mineral soil on the forest floor. Stoniness and soil texture were included with SOC to better characterize the sites. Soil pits were dug at long-term forested sites, long-term prairie sites, and recently forested sites. Samples were taken at depth intervals of 10 cm. Stoniness at depth was associated with grassland and recent forest cover, but not long-term forest. SOC patterns reflected litter inputs and suggest historic cycling of prairie and aspen cover on stony sites.
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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 | 0.000 | 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".