Holocene carbon storage and testate amoeba community structure in treed peatlands of the western Hudson Bay Lowlands margin, Canada
Bibliographic record
Abstract
Abstract The Hudson Bay Lowlands (HBL) stores a significant proportion of the northern peatland carbon pool, and constraints on the factors controlling local‐scale variation are needed to better predict soil carbon stocks. We investigated two treed peatland sites, a fen and a bog, to understand how local ecohydrological factors impacted long‐term carbon storage. Ecohydrological conditions were reconstructed using quantitative water table depth reconstructions from testate amoebae (TA) and broad peat type classifications. We also linked these factors and carbon storage to changes in TA community structure through the investigation of morphological and functional traits. Both sites have high rates of peat vertical accretion during the warmer Middle Holocene. A shift to a drier, Sphagnum‐dominated habitat after 7400 cal a bp at the bog site, however, led to lower apparent carbon accumulation rates (aCARs) than at the fen site. aCARs decreased with the transition to a cooler Late Holocene climate at both sites. Both sites have higher total carbon masses (kg m−2) than other more open and younger HBL localities, demonstrating the potential importance of treed peatlands in regional carbon storage. Shifts in the frequency of TA traits corresponded to changing ecohydrological conditions and provided insights into the role of TA in carbon storage.
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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 | 0.001 |
| 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".