Spatial patterns of vegetation structure and structural diversity across edges between forested wetlands and upland forest in Atlantic Canada
Bibliographic record
Abstract
Forested wetlands are an integral but understudied part of heterogeneous landscapes in Atlantic Canada, although they are known to provide habitat for species at risk. Our objectives were to explore patterns of forest structure across edges between forested wetland and upland forest, to locate changes in vegetation structure and to assess multivariate relationships in vegetation structure. Our study sites were in temperate (Acadian) forested wetland landscapes. We sampled trees and recorded canopy cover every 20 m along 120-m-long transects. We estimated the cover of trees, saplings, shrubs in three height classes, Sphagnum, other bryophytes, lichens, graminoids, ferns, and forbs in contiguous 1 m × 1 m quadrats. We calculated structural diversity using the Shannon index and used wavelet analysis to assess spatial patterns. We found few clear patterns except for lower tree structural diversity at the edge of forested wetlands. Structural diversity was not a reliable measure for distinguishing forested wetland from upland forest. Forested wetlands are an integral part of many forested landscapes in Atlantic Canada, but their detection and differentiation from surrounding ecosystems can be difficult. Policy should err on the side of caution when mapping forested wetlands and include them in wetland protection.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".