Restoration of slash pile burn scars to prevent establishment and propagation of non-native plants
Bibliographic record
Abstract
Logging and burning of the resultant woody debris is a management tool to reduce fire risk. Burning of the debris as piles affects the underlying soil biota and soil physical and (or) chemical properties. The resulting disturbance created by the burns may create opportunities for the establishment and spread of non-native plant species. Here, we test three restoration treatments on recent, approximately 1-year-old, pile burn scars, including an arbuscular mycorrhizal fungal (AMF) inoculant (present or absent), a ground cover (straw or no straw added), and different seeding types (native seed mix, agronomic seed mix, and no seed). The most effective treatment in reducing undesired non-native species cover was the seeding of agronomic species; here “native” and “non-native” groups exclude sown agronomic species. Undesired non-native cover was 15.1% in plots with no seed, 9.1% in plots with native seed added, and 3.5% in plots with agronomic seed added. Total vegetation cover, mostly through the increase of agronomic species, was increased by seeding and by the application of straw cover. Commercial AMF inoculum was an ineffective treatment, suggesting that a better understanding of host specificity is warranted. Restoration efforts should be directed at burn scar sites after burning to ameliorate the effects of invasive species colonization, and the use of agronomic species may prevent non-native invasive plants from establishing.
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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 | 0.001 |
| 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".