Characterization of jellyfish movement, distribution patterns and biogeochemical importance using multi-platform remote sensing observations
Bibliographic record
Abstract
The study of jellyfish blooms, which comprise large amounts of individuals that spread over broad areas, is a multi-scale scientific endeavor. Focusing on seasonal blooms of the scyphozoan jellyfish Rhopilema nomadica in the eastern Mediterranean, we show how integration of remote sensing observations from multiple platforms enables a broad perspective on jellyfish blooms, providng new insights over a wide range of spatial and temporal scales - from the behavior of individuals to the spatial characteristics and biogeochemical importance of the bloom as a whole. At the smallest scale, jellyfish swimming behavior is characterized through Lagranian tracking the trajectories of multiple adjacent individuals as appear in videos taken by drones hovering over the bloom. Results from this analysis show aggregated jellyfish exhibit distinct directional swimming behavior, which is oriented away from the coast and against the direction of surface gravity waves. At the regional scale, time varying spatial characteristics of the jellyfish bloom are extracted from aerial images taken from light airplanes. Based on the images we estimate the biomass of the jellyfish comprising the bloom, and characterize the way it is distributed along the coast. Finally, based on comparison with consecutive satellite images of surface chlorophyll concentrations, which is used as a tracer to transport by the currents, we link the displacement of the jellyfish swarm to fine scale (~1-100 km) circulation patterns. This research sheds new light on the characteristics of Rhopilema nomadica blooms in the eastern Mediterranean, and emphasises the advantages of incorporating multi-platform remote sensing observations in regional studies of jellyfish blooms worldwide.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".