Diversity of movement patterns of Longnose Gar tracked in coastal waters of western Lake Ontario
Bibliographic record
Abstract
Abstract Understanding the spatial ecology of fish is critical for effective conservation and management. Gar (Lepisosteidae spp.) are an ancient lineage of freshwater fishes that have long been thought of as having high site fidelity with limited large-scale movements. We acoustically tagged Longnose Gar (L. osseus) in a large freshwater embayment (Hamilton Harbour) and tracked the movements of 12 individuals within this system and throughout the coastal waters of Lake Ontario for up to 4 years. Three distinct movement patterns were observed: migrants (N = 7), which moved throughout much of western Lake Ontario and had a maximum displacement of 184 km (linear distance); residents (N = 3), which remained in or near the original tagging location (an industrial slip with year-round warmwater outfall); and sporadic migrants (N = 2), which showed both migratory and resident behavioural patterns in different years. Seasonally, gar were most active during the summer and then fall, with more restricted home ranges and movements during winter suggestive of more quiescent behaviour. This study represents the first use of acoustic telemetry to track Longnose Gar movements year-round in a large lake and confirmed that a majority of tracked fishes make large-scale movements in Lake Ontario. Such results reframe the presumption that gar are more resident fishes with restricted space use.
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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".