Effects of land use disturbance on seed banks of riparian forests in southern Manitoba
Bibliographic record
Abstract
Riparian forests have been adversely affected by human land use and are threatened across North America. Seed banks play an important role in the maintenance and regeneration of forests, yet effects of land use and fragmentation on forest seed banks remain poorly understood. In 1998 and 1999, we assessed impacts of human disturbance on the diversity and species composition of seed banks in upland portions of riparian forests along an urban-rural gradient in southern Manitoba. Twenty-five forest fragments were categorized according to the following land-use: urban, suburban, high-intensity rural, low-intensity rural, and relatively undisturbed reference classes. Seeds of weedy and exotic species were positively associated with fragmentation, high levels of disturbance, and dry alkaline soils. Seed bank species diversity was lower in urban sites than in rural sites, and the similarity of urban to reference sites was significantly lower than that of rural to reference sites. In contrast, the proportion of exotic to native species richness was highest in seed banks of urban sites. Exotic species Hackelia virginiana and Poa pratense were associated with urban and suburban sites, respectively. Six exotic species were unique to urban sites; these included Hesparis matronalis and Plantago major. In contrast, many of the frequently encountered native species were absent from urban sites; these included Anemone canadensis and Rubus idaeus. These changes in seed bank may affect the ability of riparian forests to recover from adverse impacts associated with urban development and agriculture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".