Ocean transport and vertical mixing connect Greenland's macroalgae to deep ocean carbon sinks
Bibliographic record
Abstract
Macroalgal carbon export estimates make assumptions about lateral transport away from the coast and vertical export to deep ocean sinks. Yet, few studies have resolved these pathways. This paper tests lateral surface transport and vertical export assumptions using the Southwest Greenland continental shelf and the Labrador Sea as a testbed. Macroalgae grow on Greenland's rocky shoreline and previous studies have documented oceanographic connectivity between coastal and offshore regions. This study analyzed 1380 Sentinel-2 satellite images to find 7973 patches of floating macroalgae on the SW Greenland shelf and in the Labrador Sea, providing evidence of their presence on the shelf and offshore waters. Since satellite imagery provides a snapshot of macroalgal positions at a given time, 305 surface drifter trajectories and a Lagrangian particle tracking model (LPTM) are used to quantify residence times and transport pathways. The average drifter-derived surface residence times on the SW Greenland shelf and the Labrador Sea are 12.1 days and 63.6 days, respectively. Applying results from studies of macroalgal longevity, the drifter-derived residence times suggest that macroalgae can remain intact during their transit of the shelf, allowing them to sink in deeper water offshore. The LPTM traced the origins and pathways of selected patches in June 2018. To explore vertical export mechanisms, a Large Eddy Simulation revealed that deep convection can transport buoyant macroalgae to depths where their gas vesicles implode, expediting sinking. These interdisciplinary findings indicate that Greenland's macroalgal ecosystems can supply detrital carbon to the adjacent shelf and open ocean and highlight the importance of testing key transport assumptions used to estimate macroalgal contributions to carbon sequestration.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".