Do power line rights-of-way facilitate the spread of non-peatland and invasive plants in bogs and fens?
Bibliographic record
Abstract
Linear infrastructures are known to facilitate the spread of undesirable species in ecosystems. Compared with other types of infrastructure, the role of power line rights-of-way (ROWs) as dispersal vectors remains poorly understood, especially with regard to peatlands. The aim of our study was to evaluate their impacts on the vegetation of ombrotrophic (bog) and minerotrophic (fen) peatlands. The vegetation communities within and adjacent to power line ROWs were sampled in 23 bogs and 11 fens in southern Québec. In fens, invasive species were found in abundance along the first 250 m within ROWs, while native non-peatland species were able to spread into entire ROWs. Invasive species were also able to colonize the adjacent fen habitats but were mostly concentrated in the first 4 m from ROW edges. Some species were, however, able to establish at more than 43 m from ROWs. Invasive and native non-peatland species were mostly restricted to the first 31 m within ROWs intersecting a bog and almost none dispersed in the adjacent bog habitats. Overall, the average cover of native non-peatland and invasive species in ROWs was mostly related to intrinsic abiotic conditions such as water pH, water conductivity, and water table level, while landscape surrounding the peatland and historical variables (e.g., time elapse since the construction of the ROW) had few impacts in both bogs and fens.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".