Physical and biological roles of mesoscale eddies in Japanese eel larvae dispersal in the western North Pacific Ocean
Bibliographic record
Abstract
The physical and biological roles of mesoscale eddies in Japanese eel larvae dispersal are investigated using a three-dimensional (3D) particle-tracking method, with a focus on the Subtropical Counter Current eddies of the western North Pacific Ocean. Virtual eel larvae (v-larvae) movements depends on the 3D ocean currents and active swimming behavior, including vertical swimming (diel vertical migration), horizontal directional swimming toward settlement habitat, and horizontal swimming toward available food. V-larvae are able to remain in eddies passively due to mesoscale eddy nonlinearity and/or actively due to attraction to rich food supplies. Thus, both physical trapping and biological attraction to food contribute to the retention of v-larvae in eddies. Physical trapping dominates the retention of v-larvae whose swimming speeds are slower than the eddy propagation speed, whereas biological food attraction prevails in the retention of v-larvae swimming faster than eddy propagation. Food availability differs between warm (anti-cyclonic) and cold (cyclonic) eddies, with the latter providing a richer food supply. Fish larvae that are retained for longer durations in cold eddies (shorter durations in warm eddies) are able to obtain more food and potentially grow faster, which enhances survival rates.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".