Holocene regional climate change and formation of southern Ontario's largest swamp inferred from a kettle-lake pollen record
Bibliographic record
Abstract
Abstract Greenock Swamp wetland complex is one of few remaining natural wetlands in the Great Lakes region and, at 89 km2in areal extent, is currently the largest hardwood swamp in southern Ontario, Canada. We present here pollen and sediment records from a kettle hole (Schmidt Lake) and adjacentThuja occidentalisswamp to reconstruct regional paleoclimate and vegetation history, and to assess the timing and development of the swamp ecosystem and associated carbon stocks. Pollen-inferred paleoclimate reconstructions show the expected warming in the Early Holocene, and indicate the Mid-Holocene initiation of lake-effect snow. This enhanced snowfall may have maintained high water tables in the adjacent wetland since ca. 8300 years ago, promoting the establishment of a swamp dominated byThuja occidentalis. Carbon accumulation rates in a >2-m-long peat core collected from aThuja occidentalisstand adjacent to Schmidt Lake are 30–40 g C/m2/yr, which is higher than the average of northern high-latitude peatlands. Using topographic and hydrological parameters, we estimated that mean swamp peat thicknesses could exceed 2 m. Thus, this study encourages future investigations on temperate swamps from the perspective of hitherto underestimated Holocene carbon sinks and shows the importance of regional hydroclimate in supporting swamp ecosystems.
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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 | 0.001 | 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".