An integrated Dissolved Organic Carbon Dynamics Model (DOCDM 1.0): model development and a case study in the Alaskan Yukon River Basin
Bibliographic record
Abstract
Abstract. Quantitative understanding of the variation in dissolved organic carbon (DOC) is important to studying the terrestrial ecosystem carbon cycle. This study presents a process-based, dissolved organic carbon dynamics model (DOCDM 1.0) that couples the soil heat conduction, water flow, DOC production, mineralization and transport in both surface and subsurface of soil profile to quantify DOC dynamics in boreal terrestrial ecosystems. The model is first evaluated and then applied for a watershed in Alaska to investigate its DOC production and transport. We find that 42 and 27 % of precipitation infiltrates to soils in 2004, a warmer year, and in 1976, a colder year, respectively. Under warming conditions, DOC transported via overland flow does not show the expected decrease trend while the overland DOC yield shows a 4 % increase. The horizontal subsurface flow only accounts for 1–2 % of total water flux, but transports 30–50 % of DOC into rivers. Water flush due to water infiltration controls DOC transport. Snowmelt plays a noticeable role in DOC flush-out and DOC transport significantly depends on flowpaths in the study region. High soil temperature stimulates DOC production. The overland DOC export does not necessarily follow the DOC downward trend in surface water transport. Overall, this study shows that DOC export behavior is complex under changing temperature and hydrological conditions in cold-region watersheds. To adequately quantify DOC dynamics in northern high latitudes, more DOC and hydrological data are needed to better parameterize and test the developed model before extrapolating it to the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".