Combining remote sensing and in situ observations to study the physical-biological coupling at fine scale: recent Mediterranean campaigns and outlook.
Bibliographic record
Abstract
The oceanic fine scales are highly energetic features (eddies, fronts, meanders, filaments) with relatively short lifetimes (days/weeks to months). Due to their associated strong gradients in physical and biogeochemical properties, they crucially affect ocean physics and ecology with potential impacts at the climate scale. The temporal scale associated with these horizontal and vertical fine scales is the same as many important ecological processes including phytoplankton growth and competition. This temporal resonance is one of the reasons behind the fine-scale variability appearing in the marine ecosystems structure and related domains, including biogeochemical cycles, trophic food-webs up to resources and biodiversity. Over the past few decades, great progresses have been made in characterizing fine scales through modeling. Remote sensing is also improving rapidly in terms of resolution, with landmark missions like SWOT expected to be operational very soon (2022). However, in situ sampling remains challenging due to the difficulties of mapping a large domain covering the length of a filament or the diameter of an eddy (~100km) at high spatio-temporal frequency (~km and ~daily). Here we present some sampling strategies we are developing for addressing this issue by combining remote sensing and in situ multi-platform high-resolution sampling of physical, biogeochemical and biological variables. In a series of campaigns in the Mediterranean Sea (OSCAHR 2015, PROTEVSMED-SWOT 2018, FUMSECK 2019), satellite-based adaptive and Lagrangian strategies proved to be successful to target and follow fine scale structures in situ. When paired with in situ biological measurements, like automated cytometry, these strategies highlight the important role of the fine scales in structuring the phytoplankton community by acting as fluid dynamical barriers and biodiversity hot-spots. To extend these observations to other regions, we support an international coordinated experimental effort of the fine scale community at several sites all around the world to fully exploit the great opportunities offered by the launch of the satellite SWOT.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".