Spatiotemporal evolution of paludification associated with autogenic and allogenic factors in the black spruce–moss boreal forest of Québec, Canada
Bibliographic record
Abstract
Abstract Paludification is the most common process of peatland formation in boreal regions. In this study, we investigated the autogenic (e.g., topography) and allogenic (fire and climate) factors triggering paludification in different geomorphological contexts (glaciolacustrine silty-clayey and fluvioglacial deposits) within the Québec black spruce (Picea mariana)–moss boreal forest. Paleoecological analyses were conducted along three toposequences varying from a forest on mineral soil to forested and semi-open peatlands. Plant macrofossil and charcoal analyses were performed on basal peat sections (≤50 cm) and thick forest humus (<40 cm) to reconstruct local vegetation dynamics and fire history involved in the paludification process. Results show that primary paludification started in small topographic depressions after land emergence ca. 8000 cal yr BP within rich fens. Lateral peatland expansion and secondary paludification into adjacent forests occurred between ca. 5100 and 2300 cal yr BP and resulted from low-severity fires during a climatic deterioration. Fires that reduced or eliminated entirely the organic layer promoted the establishment ofSphagnumin microdepressions. Paludification resulted in the decline of some coniferous species such asAbies balsameaandPinus banksiana. The paleoecological approach along toposequences allowed us to understand the spatiotemporal dynamics of paludification and its impacts on the vegetation dynamics over the Holocene.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".