Dissolved organic carbon and dissolved organic nitrogen export from forested watersheds in Nova Scotia: Identifying controlling factors
Bibliographic record
Abstract
Riverine nutrient export represents a transfer of terrestrial nutrients to lakes, estuaries and the near‐coastal zone. In this study, we constructed regional predictive models for riverine dissolved organic carbon (DOC) and organic nitrogen (DON) exports. We used a subset of 10 watersheds to construct regional empirical models of DOC and DON export, reserving two watersheds for testing the predictive ability of each model. For the subset of 10 watersheds, mean watershed soil column C:N ratio explained 75% of the variance in DOC export and 73% of the variance in DON export (p < 0.01). Organic C:N explained 63% and 71% of the variance in DOC and DON exports, respectively. There was a stronger relationship between riverine DOC:DON ratio and mineral soil C:N (R 2 = 0.77 p < 0.001) than with organic C:N (R 2 = 0.49 p < 0.05), suggesting that de‐coupling of DOC and DON dynamics in rivers may occur when hydrologic flow paths favor organic layers. We suggest that mean watershed soil C:N ratio is likely to be an integrator of several controls on riverine DOC export including temperature and precipitation (climatic control), soil texture and nutrient status (edaphic control), vegetative species and their associated micro‐flora (biological control) and watershed topography (topographical control). Soil C:N appears to be a useful tool for predicting variability in both DOC and DON flux at a regional scale.
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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".