Restoration and seasonal effects on seed bank composition and diversity in urban meadows
Bibliographic record
Abstract
Seed bank monitoring of planted urban greenspaces is a powerful tool to evaluate restoration efforts aimed at enhancing native biodiversity. By tracking native seed persistence and non-target species reduction, seed banks offer insight into resilience against future biological invasions. Using soil seed banks, we investigated how above-ground restoration management throughout a 16 km infrastructure corridor in Toronto (Canada) impacts seedling recruitment of native seed mix species and invasive species colonization. Soil cores were taken in spring and fall from 80 plots across nine meadow locations at two restoration stages: (1) newly-established sites receiving preparatory rototilling, cover crop and native seed application; and (2) restored sites alternating between maintenance mowing treatments. In 100 days following transplantation into greenhouse trays, a total of 11 373 seedlings representing 93 taxa of forbs, grasses, and woody plants were surveyed. Restored meadows showed 30% more species, 3 times more seeds, and 9 times the proportion of targeted native seed mix species than newly-established habitats. Spring sampling of restored plots had 0.8 times more seedlings and 1.5 times higher proportion of invasive species compared to fall. Through soil preparation and native seeding, restored sites build up native seed banks, reducing spontaneous colonization opportunities for invasive 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 | 0.000 |
| 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".