Simulating the Oceanic Migration of Silver Japanese Eels
Bibliographic record
Abstract
The oceanic migration of silver Japanese eels starts from their continental growth habitats in East Asia and ends at the spawning area near the West Mariana Ridge seamount chain. However, the actual migration routes remain unknown. In this study, we examined the possible oceanic migration routes and strategies of silver Japanese eels using a particle tracking method in which virtual eels (v-eels) were programmed to move vertically and horizontally in an ocean circulation model (Japan Coastal Ocean Predictability Experiment 2, JCOPE2). Four horizontal swimming strategies were tested: random heading, true navigation (readjusted heading), orientation toward the spawning area (fixed heading), and swimming against the Kuroshio. We found that all strategies, except random swimming, allowed v-eels swimming at 0.65 m s-1 to reach the spawning area within eight months after their departure from the south coast of Japan (end of the spawning season). The estimated minimum swimming speed required to reach the area spawning within eight months was 0.1 m s-1 for true navigation, 0.12 m s-1 for constant compass heading, and 0.35 m s-1 for swimming against the Kuroshio. The lowest swimming speed estimated from tracked Japanese eels at sea was 0.03 m.s-1, which would not allow them to reach the spawning area within eight months, through any of the tested orientation strategies. Our numerical experiments also showed that ocean circulation significantly affected the migration of Japanese v-eels. A strong Kuroshio could advect v-eels further eastward. In addition, western Pacific ocean currents accelerated the migration of navigating v-eels. The migration duration was shortened in years with a stronger southward flow, contributed by a stronger recirculation south of Japan, an enhanced subtropical gyre, or a higher southward Kuroshio velocity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".