Predictive Displacement Theory (PDT): An AI-Assisted Framework for Forecasting Jellyfish Movement Based on Citizen Observations and Environmental Drivers
Bibliographic record
Abstract
Jellyfish blooms are increasing in frequency and intensity across the Mediterranean Sea, posing growing challenges to tourism, fisheries, public safety, and coastal ecosystem monitoring. Despite the rise of citizen science platforms and the availability of real-time environmental data, no operational system currently exists to forecast jellyfish movement. This paper introduces the Predictive Displacement Theory (PDT), the first proposed framework for forecasting jellyfish drift by combining user-submitted sightings with environmental drivers such as wind, wave direction, sea surface currents, and atmospheric pressure. The concept is designed to operate through an AI-assisted application that ingests real-time observations and oceanographic data to generate short- and medium-term forecasts of jellyfish aggregations. As a proof of concept, the framework was retrospectively tested on the 2020~2023 Pelagia noctiluca blooms in Greece, with a focus on the Corinthian Gulf during 2021 and 2022, using Windy.com datasets and geo-referenced observations from the iNaturalist platform and a Facebook group. Even without AI support, the model predicted southward jellyfish movement with up to 90% accuracy over five-day periods. These findings demonstrate the viability of PDT and its potential to evolve into the first real-time jellyfish forecasting system, supporting both ecological forecasting and timely public warning mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".