Pollen-Derived Paleovegetation Reconstruction and Long-Term Carbon Accumulation at a Fen Site in the Attawapiskat River Watershed, Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
The Hudson Bay Lowlands (HBL) constitute a globally significant carbon pool; the paleoecological record provides an opportunity to investigate long-term drivers of change in carbon accumulation and related changes in vegetation. We present a Holocene record from the Victor Fen site (VM-3-3) in Ontario’s HBL to reconstruct vegetation history, quantify rates of carbon accumulation, and determine the role of paleoclimatic drivers. Pollen analysis indicates initiation of peat accumulation over a mineral substrate, accompanied by relatively rapid rates of carbon accumulation, following emergence from the Tyrrell Sea ∼6900 yrs BP. The earliest vegetation assemblage consisted of a tidal marsh, quickly succeeding to a Typha marsh, then a poor fen dominated by Sphagnum and Cyperaceae by 6400 yrs BP. Rapid rates of isostatic uplift at the time likely contributed to these changes. Once established, this fen community persisted without major vegetation change until the most recent century, when the abundance of shrub and Cyperaceae pollen increased, suggesting increasingly minerotrophic conditions. Average rate of long-term carbon accumulation (LORCA) for the whole record (mean = 22.8 g C m-2 yr-1) is similar to other northern peatlands, and higher than the Holocene average for an adjacent bog. Increased precipitation after ∼2400 yrs BP may have contributed to the higher LORCA reconstructed for the late Holocene, but the increased precipitation did not coincide with any apparent changes in vegetation as inferred from pollen assemblages.
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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 | 0.001 | 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".