Resilience and Adaptation: Yukon River Watershed Contaminant Risk Indicators
Bibliographic record
Abstract
River watersheds are among the most complex terrestrial features in Alaska, performing valuable ecosystem functions and providing services for human society. Rivers are vital to both estuarine and aquatic biota and play important roles in biogeochemical cycles and physical processes. The functions of watersheds have been used as vulnerability indicators for ecosystem and socioeconomic resilience. Despite a long history of human activity, the Yukon River has not received the holistic and interdisciplinary attention given to the other great American river systems. By using hypothesis-based monitoring of key watershed functions, we can gain insight to regime-shifting stresses such as fire, toxins, and invasive species development. Coupling adaptive risk management practices involving stakeholders with place-based education, especially contaminants and nutrition related, can maintain resilience within communities. The Yukon watershed provides a broadscale opportunity for communities to monitor the environment, manage resources, and contribute to stewardship policy formation. Monitoring keystone species and community activities, such as citizen science, are critical first steps to following changes to resiliency throughout the Yukon watershed. Creating a policy environment that encourages local experimentation and innovation contributes to resilience maintenance during development-imposed stress.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".