Predicting export of dissolved organic carbon from forested catchments in glaciated landscapes with shallow soils
Bibliographic record
Abstract
This study presents a simple model of dissolved organic carbon (DOC) loading to surface waters that is applicable to headwater catchments in forested regions on glaciated landscapes. Average annual DOC export was highly variable among the 33 experimental catchments along an east‐west transect, ranging from 0.90 to 13.74 g C/m 2 /a. It was hypothesized that the proportion of wetlands within the catchments would explain the majority of variation in average annual DOC export. To test this hypothesis, digital terrain analysis was used to derive wetlands automatically under both open and closed forest canopies by identifying the probability of a grid cell being a depression and/or flat. Using a 10 m digital elevation model (DEM) derived from readily available sources, the proportion of wetlands explained 63% of the variance in average annual DOC export among the 33 experimental catchments. Inclusion of regional climatic indicators, including the number of growing degree days (with a base of 10°C) and the runoff coefficient, increased explanation of variance from 63% to 89%, once catchments with lakes (>5% of catchment area) adjacent to the catchment outlets were removed. This study shows that DOC export can be predicted accurately from headwater catchments in forested regions on glaciated landscapes using a simple model based on the proportion of wetlands and easily calculated climatic variables.
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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 | 0.000 |
| 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) | 0.000 | 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".