Strategic needs of water on the Yukon: An interdisciplinary approach to studying hydrology and climate change in the Lower Yukon River Basin
Bibliographic record
Abstract
Strategic Needs of Water on the Yukon (SNOWY) is an interdisciplinary research project funded by the National Science Foundation (NSF; http://www.nsf.gov/). The SNOWY team is made up of a diverse group of researchers from different backgrounds and organizations. This partnership between scientists from different disciplines (hydrology, geography, and social science), government agencies, nonprofit organizations, universities, and Lower Yukon River Basin (LYRB) and Yukon-Kuskokwim (YK) Delta communities provided an opportunity to study the effects of climate change using a holistic approach. The Arctic and Subarctic are experiencing environmental change at a rate faster than the rest of the world, and the lack of historical baseline data in these often remote locations makes understanding and predicting regional climate change difficult. This project focused on collecting data to fill in these gaps by using both quantitative and qualitative methodologies to tell the story of environmental change in this region as told by the physical data and the people who rely on this landscape.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".