The Use of Biotelemetry in Management of Areas of Concern in the Laurentian Great Lakes
Bibliographic record
Abstract
Substantial efforts have been made to rehabilitate freshwater ecosystems and fish populations around the globe.In this thesis, I illustrate how biotelemetry can be used to complement traditional fish sampling methods to guide efforts to rehabilitate fish populations and habitat.I highlight several case studies within the Laurentian Great Lakes where biotelemetry is being used at various planning and monitoring stages, and used biotelemetry to monitor and inform the fish rehabilitation efforts in Hamilton Harbour, Lake Ontario.Walleye (Sander vitreus) have been reintroduced into the Harbour during 1992-2016.Biotelemetry revealed that mature walleye spent the majority of the study (October 2015-2016) within the Harbour, did not migrate out of the Harbour during spring spawning season, and that their home range extent was significantly reduced in the summer.These findings provided locations for future stocking and natural recruitment research and guided further research into the effects of summer hypoxia on walleye movements.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".