Lateral and vertical organic matter dynamics in an Arctic lagoon system
Bibliographic record
Abstract
Increasing warming causes severe environmental changes in the Arctic coastal zone such as longer open water periods and rising sea levels. These processes intensify the erosion of permafrost coasts and lateral transport of sediment and organic matter (OM). Lagoons play a particularly important role in the transfer process of terrestrial OM but have been rarely investigated in the Arctic. Here, we studied a lagoon system along the Arctic Yukon coast to better understand the lateral pathways of OM from land to sea and its deposition dynamics over time. We sampled terrestrial, lagoon and marine sediment to track OM along a land-lagoon-ocean transect and took short cores to assess OM deposition dynamics. Samples were analysed for total organic carbon and nitrogen (TOC, TN), stable carbon and nitrogen isotopes (δ13C, δ15N), as well as grain size and surface area. We further analysed the shoreline change rates of the lagoon from 1950s to 2018 and coupled it to sedimentation rates derived from 210Pb/137Cs dating. Turbidity was estimated in the lagoon surface water using Landsat imagery for the main wind directions. Our results show that OC concentrations significantly decrease along the land-lagoon-ocean transect. Currents potentially removed large portions of eroded OM, especially under easterly winds, which is indicated by elevated SPM concentrations. In contrast, OM can get buried quickly, which is indicated by high OM contents in deeper lagoon sediments. Coastal erosion rates in the lagoon increased drastically since the 1970s and correspond with increasing sedimentation rates, suggesting a direct relation of environmental forcing and OM deposition dynamics in the lagoon. We conclude that lagoons are a crucial transfer zone between land and ocean, which can substantially influence OM pathways. Under current environmental change scenarios in the Arctic, the role of lagoons may get more important as gateways of OM from land to sea.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".