Exploring the role of the physical marine environment in silver eel migrations using a biophysical particle tracking model
Bibliographic record
Abstract
Abstract Both the American eel (Anguilla rostrata) and European eel (Anguilla anguilla) undertake long-distance migrations from continental waters to their spawning sites in the Sargasso Sea. Their migration routes and orientation mechanisms remain a mystery. A biophysical particle tracking model was used in this study to simulate their oceanic migration from two release areas: off the Scotian Shelf (Canada) and off the Irish continental shelf. Two plausible swimming-directed behaviours were considered for simulating two different migratory paths: true navigation to specific spawning sites and innate compass orientation towards the vast spawning area. Several combinations of swimming speeds and depths were tested to assess the effect of ocean circulation on resulting migratory pathways of virtual eels (v-eels), environmental conditions experienced along their oceanic migration, and energy consumption. Simulations show that the spawning area can be reached in time by constantly swimming and following a readjusted heading (true navigation) or a constant heading (compass orientation) even at the lowest swimming speed tested (0.2 m s−1) for most v-eels. True navigation might not be necessary to reach the spawning area. The ocean currents affect mainly the migration of American v-eels, particularly for swimming speeds lower than 0.8 m s−1. The ocean circulation increases the variability in the oceanic migration and generally reduces the efficiency of the v-eels, although positive effects can be possible for certain individuals. The depth range of diel vertical migration (DVM) significantly affects the total energy expenditure due to the water temperature experienced at the various depths. Model results also suggest that energy would not be a limiting factor as v-eels constantly swimming at 0.8 BL s−1 spent <25 and 42% of energy available for migration for American and European v-eels, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".