Phytoplankton reaction to an intense storm in the northwestern Mediterranean Sea
Bibliographic record
Abstract
Abstract. The study of extreme weather events and their impact on ocean physics and biogeochemistry is challenging due to the difficulty of collecting in situ data. Yet, recent research pointed out the major influence of such physical forcing events on microbiological organisms. Moreover, such violent event occurrences may rise in the future in the context of global change. In May 2019, an intense storm occurred in the Ligurian Sea (north-western Mediterranean Sea) and was captured during the FUMSECK cruise. In situ multi-platform measurements (vessel-mounted ADCP, thermosaligraph, fluorometer, flow cytometer, a Moving Vessel Profiler equipped with a multi-sensor towed vehicle, and glider) along with satellite data and a 3D atmospherical model were used to characterise the fine-scale dynamics occurring in the impacted oceanic zone. The most affected area was marked by a lower water temperature (1 °C colder), and an increase by a factor two in surface chlorophyll-a and seven in nitrate concentrations, exhibiting strong gradients with respect to the surrounding waters. Our results show that this storm led to a deepening of the mixed layer depth from 15 to 50 m and a dilution of the deep chlorophyll maximum. As a result, the surface phytoplankton biomass of most groups identified by automated flow cytometry increased by up to a factor of two. Conversely, the phytoplankton carbon-chlorophyll ratio of most groups dropped down by a factor of two, evidencing significant changes in the phytoplankton cell compositions. These results suggest that the role of storms on the biogeochemistry and ecology of the Mediterranean open sea may be underestimated and highlight the need for high-resolution measurements coupling physics and biology during these events.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".