Paleoenvironmental records of water level and climatic changes from the middle to late holocene at a Lake Erie coastal wetland, Ontario, Canada
Bibliographic record
Abstract
Abstract Pollen and diatom assemblages, and peat stratigraphies, from a coastal wetland on the northern shore of Lake Erie were used to analyze water level and climatic changes since the middle Holocene and their effects on wetland plant communities. Peat deposition began 4700 cal yr B.P. during the Nipissing II transgression, which was driven by isostatic rebound. At that time, a diatom-rich wild rice marsh existed at the site. Water level dropped at the end of the Nipissing rise at least 2 m within 200 yr, leading to the development of shallower-water plant communities and an environment too dry for most diatoms to persist. The sharp decline in water level was probably driven primarily by outlet incision, but climate likely played some role. The paleoecological records provide evidence for post-Nipissing century-scale transgressions occurring around 2300, 1160, 700 and 450 cal yr B.P. The chronology for these transgressions correlates with other studies from the region and implies climatic forcing. Peat inception in shallow sloughs across part of the study area around 700 cal yr B.P. coincides with the Little Ice Age. These records, considered alongside others from the region, suggest that the Little Ice Age may have resulted in a wetter climate across the eastern Great Lakes region.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".