Using Water Table Depths Inferred From Testate Amoebae to Estimate Holocene Methane Emissions From the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Abstract Wetlands are the largest natural source of methane, yet the roles of source region and paleoclimate in explaining the variability in Holocene atmospheric methane concentrations remain poorly constrained. The Hudson Bay Lowlands (HBL) is one of the world's largest continuous peatland regions and a significant source of methane. We present here, using a novel proxy‐based approach, Holocene methane fluxes for the HBL. Paleo‐methane fluxes were quantified based on water table depth (WTD), inferred from testate amoeba assemblages in nine peat records. WTDs were reconstructed using a North American transfer function and were used to estimate paleo‐methane flux through a linear regression model of contemporary growing season methane fluxes and WTDs from 88 sites across the region. Following HBL peatland initiation in the Middle Holocene, total methane flux is closely related to the increasing area of land emerging from below sea level, controlled by rapid rates of glacial isostatic adjustment. In the Late Holocene, rates of uplift slowed, but methane fluxes remained high due to lower evapotranspiration in a wetter and cooler climate. We estimate that 4.8 ± 1.6 Pg C has been released from HBL peatlands to the atmosphere as CH4 over the last 8,000 years, with an average annual methane emission of 1.1 Tg CH4 yr−1 in the Late Holocene. The values estimated here are broadly consistent with those calculated from other independent methods, on modern and Holocene timescales, demonstrating that testate amoeba records provide an effective approach for scaling local processes to regional paleo‐methane emissions.
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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 | 0.000 |
| 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".