Poor fen succession over ombrotrophic peat related to late Holocene increased surface wetness in subarctic Quebec, Canada
Bibliographic record
Abstract
ABSTRACT Northern peatlands act as archives of environmental change through their sensitivity to water balance fluctuations, while being significant contributors to global greenhouse gas dynamics. Subarctic fens in north‐eastern Canada are characterized by a dominance of pools and flarks. We aimed to reconstruct the late Holocene hydrological conditions of these fens to establish the timing of the initiation of pool and flark formation and possible links with climate. Testate amoebae and plant macrofossils from five cores, sampled in three fens, were analysed to infer water tables with chronologies based on14C and210Pb dating. All sites showed the presence of relatively dry, ombrotrophic conditions with abundantPiceafrom 5000 cal a BP, followed by a first shift to wet, poor fen conditions with pool and flark development around 3000 cal a BP and a subsequent wet shift after ∼800 cal a BP. These trends coincide with previously observed Neoglacial and Little Ice Age cooler and wetter conditions and therefore climate may well have been a dominant factor in the initiation and development of pools and flarks over the late Holocene. The effect of anticipated climate change on subarctic peatlands remains unclear, although wetter conditions might enhance pool expansion to the detriment of terrestrial components.
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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 | 0.002 | 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) | 0.003 | 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".