Autotrophic respiration dominates ecosystem respiration at Canadian boreal forest
Bibliographic record
Abstract
Most of the Earth System Models (ESMs) rely on empirical functions for decomposition of litter with multiple soil carbon pools decaying at different rates to estimate R eco variations and its partitioning into autotrophic (R a ) and heterotrophic respiration (R h ) in relation to variation in temperature and soil water content. However, microbially-mediated litter decomposition schemes are very scarce in ESMs. Microbial enzymatic processes are integral parts in litter as well as soil organic matter (SOM) decomposition. Here we developed a mechanistic model comprising of multiple hydro-biogeochemical modules to explicitly incorporate microbially-enzymatic litter decomposition and decomposition of SOM for estimating regional-scale R a , R h and R eco . Modeled annual mean R eco values are found varying from 1000 to 8000 kg C ha -1 yr -1 in 2000-2013 within the boreal forest covered sub-basins of the Athabasca River Basin (ARB), Canada. While, for the 2000-2013 period, the annual mean R a and R h are varying within 800-6000 kg C ha -1 yr -1 and 500-3500 kg C ha -1 yr -1 , respectively. R a generally dominates R eco with nearly 30-80% contribution in most of the sub-basins in ARB. The model estimates corroborate well with the site-scale and satellite-based estimates reported at similar land use and climatic regions. Modeling the partitioning of R eco to R a , and R h are critical to understanding future climate change feedbacks and to help reduce uncertainties in ESMs in the boreal and subarctic regions.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".