Changes in Catchment‐Scale Recession Flow Properties in Response to Permafrost Thawing in the Yukon River Basin
Bibliographic record
Abstract
Abstract Permafrost influences the hydrologic response of a catchment. In this study, we test the ability of recession flow analysis to reflect thawing of permafrost at the catchment scale for the well‐studied Yukon river basin (YRB), covering large portions of Alaska, USA and parts of Canada. The changes in the recession flow properties detected in the YRB agree well with observations of permafrost thawing across central Alaska. In addition, there is good agreement between the relative increases in recession flow intercept (a proxy for effective depth to permafrost) and the relative annual increases in groundwater flow (independently assessed as a permafrost thawing effect) in the YRB catchments that have exhibited such groundwater flow increases. This study demonstrates the utility of recession flow analysis to reflect catchment‐scale changes in permafrost across a variety of permafrost conditions. The strength of this method is that it requires only daily observations of streamflow to reflect permafrost thawing on much larger measurement support scales than the local scales of direct permafrost observations. Copyright © 2009 Royal Meteorological Society
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".