The role of soil characteristics on measured and modelled carbon dioxide and energy fluxes for Arctic dwarf shrub tundra sites
Bibliographic record
Abstract
Four years of growing season eddy covariance measurements of net carbon dioxide (CO2) and energy fluxes were used to examine the similarities/differences in surface-atmosphere interactions at two dwarf shrub tundra sites within Canada’s Southern Arctic ecozone, separated by approximately 1000 km. Both sites, Trail Valley Creek (TVC) and Daring Lake (DL1), are characterised by similar climate (with some differences in radiation due to latitudinal differences), vegetation composition and structure, and are underlain by continuous permafrost, but differ in their soil characteristics. Total atmospheric heating (the sum of latent and sensible heat fluxes) was similar at the two sites. However, at DL1, where the surface organic layer was thinner and mineral soil coarser in texture, latent heat fluxes were greater, sensible heat fluxes were lower, soils were warmer and the active layer thicker. At TVC, cooler soils likely kept ecosystem respiration relatively low despite similar total growing season productivity. As a result, the 4-year mean net growing season ecosystem CO2 uptake (May 1 - September 30) was almost twice as large at TVC (64 ± 19 g C m-2) compared to DL1 (33 ± 11 g C m-2). These results highlight that soil and thaw characteristics are important to understand variability in surface-atmosphere interactions among tundra ecosystems. As recent studies have shown, winter fluxes play an important role in the annual CO2 balance of Arctic tundra ecosystems. However, flux measurements were not available at TVC and DL1 during the cold season. Thus, the process-based ecosystem model CLASSIC (the Canadian Land Surface Scheme including biogeochemical Cycles, formerly CLASS-CTEM) was used to simulate year-round fluxes. In order to represent the Arctic shrub tundra better, shrub and sedge plant functional types were included in CLASSIC and results were evaluated using measurements at DL1. Preliminary results indicate that cold season CO2 losses are substantial and may exceed the growing season CO2 uptake at DL1 during 2010-2017. The joint use of observations and models is valuable in order to better constrain the Arctic CO2 balance.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".