Seasonal hydrology and permafrost disturbance impacts on dissolved organic matter composition in High Arctic headwater catchments
Bibliographic record
Abstract
Arctic landscapes are experiencing intense warming and modification of precipitation regimes with climate change. Permafrost disturbances and climate change impacts on hydrology of Arctic watersheds are likely to modify the quantity and composition of exported dissolved organic matter (DOM). In July 2007, intense rainfall and active layer thickening caused widespread active layer detachments at Cape Bounty, Melville Island (Canada). This study investigates the impacts of seasonal hydrology and permafrost disturbance on DOM composition exported from High Arctic headwater catchments. In 2012, streams were sampled from three disturbed catchments and one undisturbed catchment. The composition of DOM was characterized using absorbance and fluorescence spectroscopy. DOM was mostly exported during the spring freshet. Throughout this period, the undisturbed catchment exported humified DOM with high humic-like fluorescence that likely originated from runoff through shallow organic rich soil. In contrast, DOM exported from disturbed catchments was fresher, less humified with a high proportion of low molecular weight humic acid. We demonstrate that disturbed catchments delivered likely more labile DOM derived from either thawed permafrost or enhanced microbial activity. If this labile DOM comes from an ancient pool, as indicated by other studies at this site, disturbances may strengthen the permafrost carbon feedback on climate 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 | 0.001 |
| 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".