Spatial distributions and edge relationships of plant communities in coastal barrens in Nova Scotia, Canada
Bibliographic record
Abstract
Abstract Context Coastal barrens support habitat types including wetlands, dwarf shrublands and tree 'islands' as well as rare plant species. Forest expansion, sea-level rise and recreational trails affect plant communities but spatial vegetation patterns within barrens are unknown.Objectives To classify plant communities; to quantify size, shape, spatial distributions, edge relationships and environmental correlates of plant community patches.Methods Landcover maps were created at three 500 m x 500 m coastal barrens sites using high-resolution multispectral aerial imagery. Community patches were compared using size and shape metrics; shared edge length identified adjacent communities. Community distributions were modelled using environmental variables such as elevation and distance to coast.Results Forty distinct plant communities were detected, with shrublands (37.5% total area), dwarf shrublands (23.3%) and bog wetlands (13.9%) being the most abundant. Average patch size was 9.2 m2; average patch density was 951 patches/ha, indicating fine scale community variability. ATV trails occurred primarily in bog wetlands. Dwarf shrublands and some wetland types were closest to the coastline; taller shrublands and tree islands occurred further from the coast. Edge relationships revealed a vegetation height gradient across the forest-barren ecotone: tree islands were mostly adjacent to tall shrub communities, followed by progressively shorter vegetation.Conclusions Topographic variability and distance to coast were important predictors of community distribution. Shrublands and dwarf shrublands dominated the sites; other types including forest and wetland were abundant in certain conditions. Edge relationships among communities allowed identification of those most at risk from trail disturbance, forestation and coastal squeeze.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".