Spatial ecology of translocated American Eel (Anguilla rostrata) in a large freshwater lake
Bibliographic record
Abstract
Abstract American Eel ( Anguilla rostrata ) undertake extensive migrations from their rearing grounds to spawn in the Sargasso Sea, and historically the upper St. Lawrence River and Lake Ontario provided an important source for large, fecund female eel. Following declines in the Lake Ontario population, glass eel were translocated from eastern Canada from 2006 to 2010. From 2016 to 2018, large, presumably translocated yellow eel ( N = 230) with the potential to begin maturing and out-migrating within their year of capture were collected in spring and fall and tagged with acoustic transmitters. Eel were released into eastern Lake Ontario and tracked to better understand their movement patterns prior to and during migration, and the timing of migration. Most eels successfully migrated out of Lake Ontario (64%). Timing of migration was consistent regardless of year or tagging season and primarily occurred in late summer or fall, with cooling water temperatures and decreasing sky illumination associated with initiation for fall tagged eel. Eels were mostly detected in eastern Lake Ontario and those in western Lake Ontario were mostly detected in shallow waters (< 20 m) close to shore. Eels were detected on fewer receivers in the winter, suggesting reduced movements during this season. Finally, larger individuals spent less time in the system, particularly when tagged in the fall. These findings confirm that translocated eels can migrate out of Lake Ontario; however, the weeks when migration occurred were more aligned with timing in their natal range (i.e., eastern Canada) than with naturally recruited eels from Lake Ontario. This temporal mismatch requires further consideration, since it may influence arrival times of translocated eel to the spawning grounds and their recruitment potential. These results can be used to inform future assessments of eel translocation efficacy and can also aid in the design of future tracking studies to more completely explore the downstream migration success of eel translocated into the highly productive waters of Lake Ontario.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".