Seasonal ice and hydrologic controls on dissolved organic carbon and nitrogen concentrations in a boreal‐rich fen
Bibliographic record
Abstract
Boreal wetland carbon cycling is vulnerable to climate change in part because hydrology and the extent of frozen ground have strong influences on plant and microbial functions. We examined the response of dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) across an experimental manipulation of water table position (both raised and lowered water table treatments) in a boreal‐rich fen in interior Alaska. DOC and TDN responses to water table manipulation exhibited an interaction with seasonal ice dynamics. We observed consistently higher DOC and TDN concentrations in the lowered water table treatment (71.7 ± 6.5 and 3.0 ± 0.3 mg−L) than in both the control (55.6 ± 5.1 and 2.3 ± 0.2 mg−L) and raised (49.1 ± 4.3 and 1.9 ± 0.1 mg L−1, respectively) water table treatments. Across all plots, pore water DOC concentrations at 20 cm increased as the depth to water table increased (R2 = 0.43, p < 0.001). DOC concentrations also increased as the seasonal thaw depth increased, with solutes increasing most rapidly in the drained plot (R2 = 0.62, p < 0.001). About half of the TDN pool was composed of dissolved organic N (DON). Inorganic N and DON were both highly correlated with changes in DOC, and their respective constraints to mineralization are discussed. These results demonstrate that a declining water table position and dryer conditions affect thaw depth and peat temperatures, and interactions among these ecosystem properties will likely increase DOC and TDN loading and potential for export in these systems.
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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 | 0.000 |
| 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 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".