Mechanical site preparation and use of non-invasive cover crops influences early-successional forest vegetation composition of a reclaimed airstrip in the Boreal Forest
Bibliographic record
Abstract
Rebuilding native forest ecosystems after industrial disturbance is key to sustainable resource development. However, self-sustaining forests do not always result from current reclamation practices, mostly due to grass-driven arrested succession. Here we assessed the interactive effects of soil treatment and cover cropping on forest succession in a recently reclaimed airstrip in western Canada. Three surface soil treatment techniques were applied in five block replicates following asphalt removal, soil decompaction, site recontouring and topsoil placement with dozers: no surface treatment (smooth), discing with agricultural disc harrows (disc), or plowing with a RipPlow™ (plow). Within each soil treatment, subplots were then either seeded with Secale cereale (fall rye), a non-invasive annual grass, or left without a cover crop. In the first 5 years after treatment, soil treatment had a much greater impact on the vegetation than cover cropping. Plowing favored tree growth while both plowing and discing treatments supported natural regeneration of seed-banking shrub species and native forb cover when compared to the smooth treatment. The smooth treatment favored grass species (mostly non-native), presumably by allowing them to spread horizontally though it also encouraged higher rates of establishment of wind-dispersed Salix species. In general, the discing soil treatment had intermediate effects on tree growth and vegetation community composition. Secale cereale suppressed non-native weeds during the early stages and disappeared towards the end of the experiment, without hindering the establishment of desirable woody species. We conclude that increasing soil surface variability through the plow treatment tested in the present investigation, and potentially aided by the addition of a non-invasive cover crop, represent a combination of reclamation strategies to promote forest development in heavily disturbed industrial sites. • Higher microtopography reduced grass dominance and Secale cereale reduced weeds. • The effects of site preparation were longer lasting than those of cover cropping. • Secale cereale suppressed weeds without harming desirable woody species growth.
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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 | 0.000 |
| 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".