Seed banks of subtropical grassland patches within an urban matrix in South Africa: reflecting the past and foretelling the future
Bibliographic record
Abstract
Urban grasslands often exist as fragmented patches with varying threat levels. These patches are generally inappropriately managed due to limited data on their responses to disturbance and seed bank regenerative potential. This study compared seed banks between non-naturally disturbed and undisturbed patches of South African subtropical grasslands in an urban environment. Standing vegetation was characterised via year-round quadrat and monthly transect sampling. The seed banks were sampled after the two main dispersal periods and assessed for species composition and density. The seed bank species (24) represented a subset (9%) of the standing vegetation, with forbs being the most speciose life form and grasses the most abundant. Disturbance had a moderate impact on seed bank density but significantly affected species composition and ordering. Clustering and ordination analyses separated seed banks based on disturbance history. However, seed banks in disturbed patches maintained the general character of the grassland type. Diagnostic, rare and threatened taxa were absent from seed banks, irrespective of disturbance history. Consequently, restoration will possibly require seed enrichment and species reintroduction rather than exploitation of natural seed banks. Identifying disturbance-sensitive species through seed bank surveys can inform grassland conservation and restoration strategies.
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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 | 0.001 | 0.001 |
| 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".