Selective Sorting and Degradation of Permafrost Organic Matter in the Nearshore Zone of Herschel Island (Yukon, Canada)
Bibliographic record
Abstract
Abstract Erosion of permafrost coasts due to climate warming releases large quantities of organic carbon (OC) into the Arctic Ocean. While burial of permafrost OC in marine sediments potentially limits degradation, resuspension of sediments in the nearshore zone potentially enhances degradation and greenhouse gas production, adding to the “permafrost carbon feedback.” Recent studies, focusing on bulk sediments, suggest that permafrost OC derived from coastal erosion is predominantly deposited close to shore. However, bulk approaches disregard sorting processes in the coastal zone, which strongly influence the OC distribution and fate. We studied soils and sediments along a transect from the fast‐eroding shoreline of Herschel Island— Qikiqtaruk (Yukon, Canada) to a depositional basin offshore. Sample material was fractionated by density (1.8 g cm −3 ) and size (63 μm), separating loose OC from mineral‐associated OC. Each fraction was analyzed for element content (TOC, TN), carbon isotopes (δ 13 C, Δ 14 C), molecular biomarkers ( n ‐alkanes, n ‐alkanoic acids, lignin phenols, cutin acids), and mineral surface area. The OC partitioning between fractions changes considerably along the transect, highlighting the importance of hydrodynamic sorting in the nearshore zone. Additionally, OC and biomarker loadings decrease along the land‐ocean transect, indicating significant loss of OC during transport. However, molecular proxies for degradation show contrasting trends, suggesting that OC losses are not always well reflected in its degradation state. This study, using fraction partitioning that crosses land‐ocean boundaries in a way not done before, aids to disentangle sorting processes from degradation patterns, and provides quantitative insight into losses of thawed and eroded permafrost OC.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".