Early trajectories of forest understory development on reclamation sites: influence of forest floor placement and a cover crop
Bibliographic record
Abstract
We tested whether direct placement of forest floor material (FFM: litter, fibric, humus layers and surface mineral horizons) and sowing of a cover crop (Melilotus officinalis) could facilitate the establishment of native forest understory species at a reclaimed coal mine in Alberta, Canada. FFM was salvaged at two depths (15 and 40 cm) from a recently harvested native aspen forest and immediately placed at the same depths on the reclamation site. Total richness (approximately 61 species in 96 subplots) was similar in each of 3 years post‐placement; total richness for all 3 years combined was 87 including 34 typical boreal forest understory species plus 30 other natives. The deeper treatment reduced cover of all species, native and non‐native species in year 1. In year 3, the deeper treatment still had lower cover of non‐native species but had higher cover of forest understory species in years 2 and 3. The deeper treatment also resulted in lower species richness per plot, but only in year 1. In year 2 (when the biennial clover was at its tall stage), the cover crop treatment was associated with lower cover of non‐native species but did not affect the cover of native forest understory species. Direct placement of FFM can help facilitate establishment of a diverse native boreal forest understory in a reclaimed landscape. Although richness and cover may be initially higher with shallower salvage and placement, deeper salvage may ultimately be better for encouraging establishment of native forest understory species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".