Long‐term changes in organic matter and mercury transport to lakes in the sporadic discontinuous permafrost zone related to peat subsidence
Bibliographic record
Abstract
Abstract Permafrost‐supported peatlands near the southern limit of permafrost are experiencing dramatic landscape changes as a result of recent climate warming, which have the potential to impact aquatic ecosystems through changes in terrestrial run‐off. Our objectives were to determine how terrestrial organic matter inputs to aquatic ecosystems in the southern Northwest Territories (Canada) changed as a result of peat subsidence, and whether terrestrial organic matter can be linked to sedimentary mercury. To accomplish this, we analyzed lipid biomarkers, lignin‐derived phenols, and other geochemical proxies in sediment cores from two lakes (KAK‐1 and TAH‐7) affected by recent peat subsidence. Both lakes experienced substantial shifts in organic matter proxies through time, but the trajectory of change differed, reflecting local heterogeneity in hydrological setting and other environmental factors. In KAK‐1, recent peat subsidence corresponded to a decrease in lignin‐derived phenol yield, increased inferred lignin oxidation, and δ15N depletion. In TAH‐7, peat subsidence was likely initiated by a local forest fire, and resulted in an increase in the n‐alkanol ratio C30/(C30 + C28) (consistent with a warmer, wetter climate), lignin‐derived phenol yield (mainly syringyls), and δ13C depletion. In TAH‐7, total mercury inputs were positively correlated to terrestrial carbon inputs, suggesting allochthonous carbon is an important vector for mercury transport to the lake. In both lakes, an increase in the C23/(C23 + C29) n‐alkane ratio was observed and suggests increased organic matter input from Sphagnum mosses. Our results demonstrate how terrestrial landscape changes occurring as a result of peat subsidence can influence carbon accumulation in aquatic ecosystems in discontinuous permafrost zones.
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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 | 0.000 | 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) | 0.000 | 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".