Differential impact of thermal and physical permafrost disturbances on High Arctic dissolved and particulate fluvial fluxes
Bibliographic record
Abstract
Climate warming and changing precipitation patterns have thermally (active layer deepening) and physically (permafrost-thaw related mass movements) disturbed permafrost-underlain watersheds across much of the Arctic, increasing the transfer of dissolved and particulate material from terrestrial to aquatic ecosystems. We examined the multiyear (2006-2017) impact of thermal and physical permafrost disturbances on all of the major components of fluvial flux. Thermal disturbances increased the flux of dissolved organic carbon (DOC), but localized physical disturbances decreased multiyear DOC flux. Physical disturbances increased major ion and suspended sediment flux, which remained elevated a decade after disturbance, and changed carbon export from a DOC to a particulate organic carbon (POC) dominated system. As the magnitude and frequency of physical permafrost disturbance intensifies in response to Arctic climate change, disturbances will become an increasingly important mechanism to deliver POC from terrestrial to aquatic ecosystems. Although nival runoff remained the primary hydrological driver, the importance of pluvial runoff as driver of fluvial flux increased following both thermal and physical permafrost disturbance. We conclude the transition from a nival-dominated fluvial regime to a regime where rainfall runoff is proportionately more important will be a likely tipping point to accelerated High Arctic change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".