Upstream Development versus Downstream Risk on Transboundary Watersheds - The Taku, Stikine and Unuk Rivers
Bibliographic record
Abstract
Transboundary water governance along the BC – Alaska border is at a crossroads. The rich ecological values of the Taku, Stikine and Unuk watersheds are threatened by multiple large mine proposals in the headwaters that pose a risk to fish, wildlife, habitat, and water quality and the livelihoods and cultures that depend on them. Organizations in Alaska are advocating for a referral to the International Joint Commission under the Boundary Waters Treaty. Taking a different approach, BC and Alaska recently signed a Memorandum of Understanding. The question remains how development in the headwaters of BC and risks in downstream Alaska can be effectively managed. About the Lecturer: Anne Muter has a background in science and law, with a focus on their intersection. Anne has an earth and ocean sciences undergraduate degree, a law degree, and a Master’s in Water Science, Policy, and Management. She splits her time between working as a litigator and advising not-for-profit organizations on water policy issues. Anne is the lead on a transboundary watersheds governance initiative for the organization Rivers Without Borders Canada. This work seeks to engage with academics and water governance practitioners to develop governance mechanism for the transboundary watersheds of north-western BC and south-east Alaska. RWB Canada’s Transboundary Watersheds Governance Initiative is being led from Vancouver by Anne. She describes herself as endlessly curious about the water cycle and is dedicated to striving for responsible watershed management. Fittingly, Anne’s background is in science and law, with a focus on their intersection. She has a natural sciences degree from the University of British Columbia, a law degree from the University of Victoria, and a Master’s in Water Science, Policy, and Management from Oxford. Anne tries to spend as much time as possible cross-country skiing, sailing, biking and hiking.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".