Tracking bowfin with acoustic telemetry: Insight into the ecology of a living fossil
Bibliographic record
Abstract
Abstract Little is known about the spatial ecology and behaviour of bowfin (Amia calva), despite the fact that it is an important freshwater carnivore, the last living member of the Amiiformes and effectively a living fossil. In the summer of 2013, acoustic telemetry transmitters were surgically implanted in ten bowfin captured in Toronto Harbour on Lake Ontario. Using a stationary acoustic telemetry array that covered most of the 18‐km2 harbour, the residency and movement patterns of bowfin were tracked from their release until November 2014. Detected bowfin ranged in size from 562 to 725 mm total length and included six males and three females (one female was not detected). Bowfin showed high site fidelity with most fish detections concentrated in embayments and within the Toronto Islands, areas characterised by relatively high stable water temperatures and submerged vegetative cover. Statistical modelling revealed that bowfin residency was significantly affected by season, body size, site‐specific estimates of vegetative cover and an interaction between body size and season. Bowfin residency increased with vegetative cover and was highest for large fish during the winter and fall months. Despite the overall high site fidelity exhibited by individuals, several bowfin were mobile over the spring and summer months and moved 5.2–12.9 km among telemetry receivers in the inner and outer harbours. The results of this study provide insight into the seasonal habitat preference, home range size and activity level of this unique fish.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".