Process-based ecosystem modeling to predict carbon dioxide fluxes in the newly flooded black spruce forest and peatland
Bibliographic record
Abstract
We developed a process-based reservoir model (“flooded” version of Forest-DNDC) to project carbon fluxes from inundated black spruce forests and peatlands over the life-time of a hydroelectric reservoir located in the Boreal biome. The reservoir model was used to examine the changes of carbon dioxide (CO2) fluxes during the first four years after inundation and to evaluate the effects of the impoundments on CO2 fluxes from the Eastmain-1 reservoir in northern Quebec, Canada. The framework for the reservoir model was Forest-DNDC, a process-based terrestrial biogeochemistry model, which supports detailed soil carbon processes from considering redox chemistry and oxygen diffusion in flooded ecosystems. We modified this terrestrial model to represent the alteration of soil and vegetation carbon processes when they are located under a water column: soil decomposition parameters were adjusted for difference rates and temperatures due to submergence and the addition of new carbon via sedimentation. Using the measured environmental conditions from 2006 to 2009, modeled daily CO2 emissions from the flooded forest averaged 0.43 g C m-2 d-1 (ranging from 0.60 to 1.07 g C m-2 d-1), and those from the flooded peatland averaged 0.49 g C m-2 d-1 (ranging from 0.63 to 0.86 g C m-2 d-1). The simulated CO2 emissions decrease with the duration of flooded condition. Our simulations resulted in smaller values than those in CO2 flux measurements by the eddy-covariance system at the surface of the reservoir, but the changing pattern over time were similar. The disagreements would stem from the model structure and measurement method: the developed model certainly lacks some processes occurring in the open water portion of the reservoir, and the measured fluxes are a function of the actual turbulent transfer and are therefore somewhat removed in time and space from the actual fluxes of CO2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".