Bibliographic record
Abstract
International freshwater treaties govern the cooperative use of waters in the world’s major shared river basins but have a poor track record when it comes to species protection. Covering over forty percent of the earth’s land surface, shared basins are highly relevant to biodiversity conservation efforts with most water treaties directly affecting species and their habitats in some way. Using the Columbia River Treaty and the river basin it governs as a case study, I focus on understanding barriers to the inclusion of species conservation in the formulation and implementation of these agreements. An opening chapter illustrates the absence of, or ambiguity regarding, species conservation in the formal texts of the global collection of agreements and describes four contributing barriers: a) complexity avoidance, b) undervalued species, c) poorly understood trade-offs, and d) institutional norms. In the second chapter, I focus on b) using a welfare economics approach to assess the capacity of the Columbia River to provide four ecosystem services derived from salmon. The approach illustrates how non-zero estimates of economic value for a species can be developed in a transboundary river basin. In Chapter 3, I focus on c) by applying multi-attribute utility optimization across salmon conservation, hydropower production, and agricultural irrigation to forecast optimal flows in the Hanford Reach segment of the Columbia River. This chapter shows how, in a simulated environment, optimization can be used to explore alternative transboundary water sharing strategies that balance trade-offs across multiple values. In Chapter 4, I focus on d) using a method called incident analysis to examine a prior conflict between Canada and the US over US efforts to conserve an endangered species of sturgeon. This study provides insights regarding the Columbia River Treaty’s adaptive capacity to respond to evolving species conservation needs.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".