Intense storms affect sinking particle fluxes after the North Atlantic diatom spring bloom
Bibliographic record
Abstract
Abstract The sinking of large particles (i.e., marine snow) has long been recognized as a key pathway for efficient particulate organic carbon (POC) export to the ocean interior during the decline of spring diatom blooms. Recent work has suggested that particles smaller than marine snow can also substantially contribute to POC export. However, a detailed characterization of small and large sinking particles at the end of blooms is missing. Here, we separately collected suspended and small and large sinking particles using Marine Snow Catchers and assessed their biogeochemical composition after the North Atlantic spring bloom in May 2021. During the three weeks of sampling, when four intense storms (maximum wind speeds 37 – 50 kts) created high turbulent energy dissipation rates and deepened the mixed layer, we observed two distinct sedimentation episodes. During the storm periods, sinking particles were dominated by small (diameter < 0.1 mm), slow-sinking (~18 m d −1 ), silica-rich particles that carried a moderate POC flux (< 6 mmol C m −2 d −1 ) to 500 m depth. Once the storms ceased, the volume of large (diameter > 0.1 mm), fast-sinking (> 75 m d −1 ), carbon-rich marine snow aggregates (not fecal pellets) increased exponentially and POC fluxes at 100 m depth were more than fourfold greater (30±12 mmol C m −2 d −1 ) than those during the previous event. The aggregates consisted of a mixed post-bloom plankton community. Our data suggest that the intense storms determined the timing, type, and magnitude of POC flux at the end of a spring phytoplankton bloom.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".