Three-dimensional influencers in the Western Baltic Sea: finite time coherent sets and their role for biological processes
Bibliographic record
Abstract
Describing and tracking three-dimensional flow structures in an ocean setting may explain elemental and biodiversity pattern. A possible tool can be finite time coherent sets. These sets are Lagrangian Coherent Structures characterized by minimal leakage and minimal exchange with their surrounding environment.In oceanic settings, they can be understood as separated waterbodies or eddies playing an important role for transport and mixing processes.Due to limited interaction with their surroundings, they even influence biological processes by providing competitive advantages for some species, for example, optimal temperature or nutrient conditions.In a case study of three-dimensional finite time coherent sets in the Western Baltic Sea in May and July 2018, we show some different impacts on biological processes:enhancement of phytoplankton growth in the set's surrounding, transport of cold nutrient rich water from shallower to deeper regions, and the formation of transient, moving dynamical niches with higher temperature inside the coherent set compared to its surrounding, prolonging the life of an existing phytoplankton bloom that is trapped during the formation of the coherent set. Moreover, different dynamical patterns can be observed inside the finite time coherent sets during their travel and lifetime. Temporal stratification and mixing inside the coherent sets suppress or enhance growth temporally and locally.In the coherent set’s surrounding, the formation of a “sticking” manifold supports the development of a local phytoplankton bloom in the upper water column.Our case study in the Western Baltic Sea provides a first step towards understanding the impact of three-dimensional coherent sets on transport processes and phytoplankton growth in the Baltic Sea, as well as, the formation of dynamical pattern inside three-dimensional coherent sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".