Accumulated state of the Yukon River watershed: Part I critical review of literature
Bibliographic record
Abstract
A consistent methodology for assessing the accumulating effects of natural and manmade change on riverine systems has not been developed for a whole host of reasons including a lack of data, disagreement over core elements to consider, and complexity. Accumulated state assessments of aquatic systems is an integral component of watershed cumulative effects assessment. The Yukon River is the largest free flowing river in the world and is the fourth largest drainage basin in North America, draining 855,000 km(2) in Canada and the United States. Because of its remote location, it is considered pristine but little is known about its cumulative state. This review identified 7 "hot spot" areas in the Yukon River Basin including Lake Laberge, Yukon River at Dawson City, the Charley and Yukon River confluence, Porcupine and Yukon River confluence, Yukon River at the Dalton Highway Bridge, Tolovana River near Tolovana, and Tanana River at Fairbanks. Climate change, natural stressors, and anthropogenic stresses have resulted in accumulating changes including measurable levels of contaminants in surface waters and fish tissues, fish and human disease, changes in surface hydrology, as well as shifts in biogeochemical loads. This article is the first integrated accumulated state assessment for the Yukon River basin based on a literature review. It is the first part of a 2-part series. The second article (Dubé et al. 2013a, this issue) is a quantitative accumulated state assessment of the Yukon River Basin where hot spots and hot moments are assessed outside of a "normal" range of variability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".