Plant community composition and tree seedling establishment in response to seeding and weeding treatments on different reclamation cover soils
Bibliographic record
Abstract
The majority of plant diversity in North American boreal forests is comprised of understory plant communities undergoing continuous interspecific competition. Anthropogenic activities, particularly surface mining, have introduced higher severity disturbances that not only remove vegetation, but also modify soil. During reclamation, soils used to cap overburden materials have inhibited native plant growth and increased weed invasion. This study aimed to examine the effectiveness of seeding and weeding treatments for promoting the establishment of native understory plant communities, as well as a common deciduous tree species, on three different reclamation cover soils (forest floor – mineral mix, peat–mineral mix, and transitional). The broadcasting of a native forb seed mix was not successful on any of the cover soils, whereas weeding affected each cover soil differently. With weeding, the forest floor – mineral mix and transitional cover soils experienced a decrease in the relative abundance of introduced forbs and an increase in the relative abundance of graminoids. The increase in graminoid cover on the forest floor – mineral mix was mostly attributed to the expansion of Calamagrostis canadensis (Michx.) P. Beauv. Overall, weeding effectively eliminated introduced plant species, allowing competitive native grasses to establish. However, weeding may have unintentionally hindered the development of a native understory plant community via the over-establishment of grass on reclamation sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".