Thawing permafrost - Sedimentary organic matter distribution across the Canadian Beaufort Sea
Bibliographic record
Abstract
The Canadian Beaufort Sea experiences substantial input of sediment, organic carbon, and nutrients due to accelerated coastal erosion and transport from the Mackenzie River (Bröder et al. 2022). Ongoing climate change intensifies these fluxes along the land-ocean continuum and, thus, affect the marine ecosystem on the shelf and deep sea. Terrestrial organic carbon (TerrOC) derived from permafrost thaw leads to uncertainties in the carbon cycle on the shelf and potentially further amplifies climate warming through the decomposition of the organic-rich material. This thesis examines the fate of TerrOC by analyzing compounds of surface sediments from across the Beaufort Shelf to address the ongoing debate on whether the Canadian Beaufort Shelf is carbon sink or source. Present data from molecular biomarkers and compound-specific radiocarbon dating on long-chain fatty acids show a decreasing influence TerrOC away from the coast with a relative increase in radiocarbon ages along some transects the river mouth towards offshore locations. Furthermore, a net carbon sourcing role of the shelf is suggested, based on the presented data. These findings add up on what was observed in previous studies but need to be further analyzed and compared with other assessed data to yield proper quantifications. The Beaufort-Mackenzie coastal margin seems to be a complex setting and further investigations on transport times and TerrOC pathways.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".