A Lagrangian strategy for in situ sampling the physical-biological coupling at fine scale : the PROTEVSMED-SWOT 2018 cruise
Bibliographic record
Abstract
The term "fine scales" is generally used to refer to the ocean processes occuring on horizontal scales smaller than 10 km and characterized by a short lifetime (days/weeks). Fine scales have been predominantly studied with numerical simulations and satellite observations which have highlighted their significant role on biological processes. Indeed, their short time scale is the same as a lot of important processes in phytoplankton dynamics. Model simulations have shown that fine scales such as fronts and filaments strongly influence the distribution of phytoplankton species. Nowadays, the combination of in situ measurements, satellite observations and model simulations is a necessity to better understand these mechanisms. However these processes are particularly challenging to sample in situ because of their size and their ephemeral nature. The PROTEVSMED-SWOT cruise was performed in the Western Mediterranean Sea, in the southern region of the Balearic Islands, onboard BHO Beautemps-Beaupré, between April 30 th and May 14 th , 2018. In order to study the influence of fine scales on the distribution of phytoplankton species, a satellite-based adaptive Lagrangian sampling strategy has been deployed in order to i) identify a fine scale structure of interest, ii) sample it at high spatial resolution the phytoplankton community, and iii) follow the evolution of this structure and the related distribution of phytoplankton. The SPASSO software package uses satellite altimetry, SST and surface Chl a concentration data to generate and provide near-real time daily maps of the dynamical and biogeochemical structures present in the area. The sampling strategy was defined in order to cross a frontal zone separating different types of water. Multidisciplinary in situ sensors (hull-mounted ADCP, a Seasoar towed fish and an automated flow cytometer installed on the seawater supply of the Thermosalinograph) were used to sample at high spatial resolution physical and biological variables. A particular attention was put in adapting the temporal sampling in different water masses to the biological time scales in order to reconstruct the phytoplankton diurnal cycle. Such a strategy was successful in sampling two different water masses separated by a narrow front and characterized by different aboundances of several phytoplankton species and functional groups. Consequently, our results highlight the role of the front on the physical and biological coupling confirming previous modelling and remote-sensing studies. The new generation of altimetric satellite, SWOT, will provide a 2D sea surface height at an unprecedented resolution and it will be a unique opportunity to better observe fine scale structures in the global ocean. Our methodology paves the way to future in situ experiments that are planned in 2022 during the SWOT fast-sampling phase, few months after its launch.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".