Drivers of Dissolved Organic Matter Quality and Concentration in a Mountainous Subarctic Watershed
Bibliographic record
Abstract
Northern permafrost regions contain vast stores of organic carbon (OC) and rapidly rising temperatures make these frozen soil OC stores increasingly vulnerable to thaw and mobilization. While considerable attention has been given to carbon export from large Arctic river systems and lowland areas, significant gaps remain in characterizing OC quality and export in headwater catchments and in alpine regions. Northern wetlands and lakes have been highlighted as critical areas for OC storage and processing, and while ubiquitous in alpine regions, there have been few studies to examine their integrated role in DOM dynamics at the watershed scale. This study examines controls on DOM quality and concentration in Wolf Creek Research Basin (WCRB), Yukon, over four years using repeat spatial sampling. Optical indices were used to assess changes in DOM quality from different landscape types including permafrost influenced alpine headwater streams, lake and wetland complexes, and the catchment outlet in a low elevation boreal forest. Results indicate that DOM export in WCRB is transport-limited with greater exports during years with greater snowpack and higher spring discharge. Principal component analysis revealed that the predominant driver of DOM quality was seasonality, but landscape type was also an important control during the open water season. High SUVA254 /HIX in headwater streams indicated primarily humic, terrestrially derived DOM while high BIX and comparatively lower SUVA254 /HIX in a mid-catchment lake indicated autotrophic production of new DOM. DOM quality at the catchment outlet reflected a mixture of upstream sources and increased influence of groundwater. The results of this study highlight the importance of evaluating DOM quality in all seasons and provide insight into the diverse nature of DOM at a watershed scale. These characterizations help to elucidate potential DOM response in a rapidly changing and understudied environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".