Tracking anguillid eels: five decades of telemetry-based research
Bibliographic record
Abstract
Advances in telemetry technologies have provided new opportunities to reveal the often-cryptic spatial ecology of anguillid eels. Herein we review 105 studies published between 1972 and 2016 that used a variety of telemetry technologies to study the movements of eels in a variety of habitats. Eight anguillid species have been tracked in three main geographical locations: Western Europe, the north-eastern part of North America and Australasia. Telemetry has proven to be an effective method for determining patterns of yellow eel movements in continental waters. It has also been used extensively to investigate the migratory behaviour of maturing eels as they leave fresh water to reach the sea. Among recent findings is the observation that downstream migration in continental waters is quite discontinuous, characterised by extended stopovers. Reconstructed migration routes in the open ocean obtained from satellite tags have provided indications of spawning areas, extensive vertical migrations and initial clues about the orientation mechanisms at sea. Telemetry studies have also revealed apparent evidence of predation by marine mammals and fish at sea, suggesting a significant natural source of mortality during the eel spawning migration. Finally, we discuss some limitations of telemetry technology and future directions, as well as associated challenges, to the developing field of eel spatial ecology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".