Vegetation of<i>Sphagnum</i>bogs in highly disturbed landscapes: relative influence of abiotic and anthropogenic factors
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Question: Has the vegetation of Sphagnum bogs been affected by more than 200 years of human activities? Location: Bas‐Saint‐Laurent region, southeastern Québec, Canada. Methods: Data (species assemblages, abiotic and spatio‐historical variables) were collected in 16 bogs ranging from 2 to 189 ha, and incorporated in a geographical information system. Major gradients in vegetation composition were identified using DCA. CCA was used to relate vegetation gradients to abiotic and spatio‐historical variables. Results: A clear segregation of species assemblages was observed, from open and undisturbed bogs to forested and highly disturbed sites. Among abiotic factors, tree basal area, water table level and peat thickness had a significant influence on plant species composition. Among spatio‐historical factors, disturbance level, area loss and fire were the most influential factors. Variance partitioning between these groups of factors suggests that spatio‐historical factors had a major influence on peatlands, representing 22% of the variation observed in the plant species assemblages while abiotic factors represent only 17% of the variation. Conclusions: The results highlight the influence of agricultural and other anthropogenic activities on plant assemblages and suggest that even wetlands apparently resistant to disturbances, such as peatlands, can be severely affected by anthropogenic factors. Plant species assemblages of ombrotrophic peatlands of the Bas‐Saint‐Laurent region were, and still are, largely influenced by human activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it