Morphometric Control on Dissolved Organic Carbon in Subarctic Streams
Bibliographic record
Abstract
Abstract Climate change has the potential to alter hydrological regimes and to expand saturated areas in permafrost environments, which are important sources of organic carbon. The sources, transfer zones, and delivery mechanisms of carbon into the stream network are controlled by the morphometric properties of the catchment; however, the utility and limitations of these properties as predictors of dissolved organic carbon concentrations have rarely been systematically evaluated. This study tested the relationships between 18 morphometric indicators and observed dissolved organic carbon (DOC) concentrations in the Stordalen catchment, Sweden. Geospatial and explorative statistics were combined to assess the topographical, areal, and linear indicators influencing the distribution of DOC in the catchment. The results suggest that catchment morphometric indicators can be used as proxies to predict DOC concentrations along a longitudinal continuum in subarctic climate regions (R2 up to 0.52). Morphometry indicators that best served as predictors of DOC concentration in the model were as follows: relief, slope length and steepness factor (LS‐factor), sediment transport capacity, and catchment area. Due to the influence that catchment form exerts in DOC spatial patterns and processing, a morphometric approach can serve as a first approximation of DOC spatial patterns within a catchment. The initial step in identifying carbon sources based on the catchment topography has the potential to allow for quick and multilevel comparison within and between catchments.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".