Vegetation of Sphagnum bogs in highly disturbed landscapes: relative influence of abiotic and anthropogenic factors
Bibliographic record
Abstract
Abstract Question: Has the vegetation of Sphagnumbogs 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. Nomenclature: Anderson et al. (1990) for mosses; Esslinger & Egan (1995) for lichens; Marie-Victorin (1995) for vascular plants. Abbreviation: GIS = Geographical information system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".