Biotelemetry informing management: case studies exploring successful integration of biotelemetry data into fisheries and habitat management
Bibliographic record
Abstract
Biotelemetry data have been successfully incorporated into aspects of fishery and fish habitat management; however, the processes of knowledge mobilization are rarely published in peer-reviewed literature but are valuable and of interest to conservation scientists. Here, we explore case examples from the Ocean Tracking Network (OTN), including Pacific salmon (Oncorhynchus spp.) in British Columbia, Canada; Greenland halibut (Reinhardtius hippoglossoides) in Cumberland Sound, Canada; and lemon sharks (Negaprion brevirostris) in Florida, USA, to document key processes for science integration. Typical recommendations documented in the literature (e.g., co-production of knowledge, transdisciplinary methodologies, applied research questions) were recorded to have had successful fisheries management integration, although we documented some exceptions. In each case, it was early, active, and ongoing communication outside of traditional science communication and the visual evidence of fish movement that were critical in engaging all parties with a vested interest. Networks offer forums for knowledge sharing on lessons learned and development of skills to engage in active communication. Greater investments and attention to develop these skills are needed to foster positive and active relationships that can impart real change in management and conservation.
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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".