Reducing Uncertainties in Managing in British Columbia Waters: Applying an Adaptive Management Mindset on the South, Central and North Coasts
Bibliographic record
Abstract
British Columbia’s vast coastline is characterized by ecologically rich, rugged, and remote regions where there are many uncertainties about the way that ecosystems function. This translates into a challenge for environmental managers, as it creates considerable uncertainty about which management actions will be most effective for achieving management goals and objectives. Adaptive management can offer a way forward by providing systematic, rigorous approach for designing and implementing management actions to maximize learning about critical uncertainties affecting decisions on environmental management policy and practice. It typically follows a six-step cycle focusing on the implementation and monitoring of management actions that are deliberately designed to reduce critical uncertainty, and adjusting management based on what is learned. In most cases, this approach relies heavily on interdisciplinary collaboration among scientists, managers, resource users, and the broader community. We showcase the application of an adaptive management mindset through three cases along the BC coast. The first is from the south coast, where stakeholders are working to assess options in the process of developing a strategic integrated management plan for Chinook salmon. The second is from the Kemano River on the central coast, where eulachon management is being informed by the evaluation of previous monitoring activities. The third is from the Skeena Estuary on the north coast, where recommendations for future data collection have been designed to address key uncertainties in the management of Pacific salmon. These stories showcase recent successes of applying an adaptive management way of thinking in the region and highlight how this approach can help to reduce critical uncertainties often cited as a barrier to the effective management of our coastal marine resources.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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".