Assessing a new soil carbon model to simulate the effect of temperature increase on the soil carbon cycle in three eastern Canadian forest types characterized by different climatic conditions
Bibliographic record
Abstract
The predictive capacity of process-based models on the carbon (C) cycle in forest ecosystems is limited by the lack of knowledge on the processes involved. Thus, a better understanding of the C cycle may contribute to the development of process-based models that better represent the processes in C cycle models. A new soil C model was developed to predict the effect of an increase in the temperature regime on soil C dynamics and pools in sugar maple (Acer saccharum Marsh.), balsam fir [Abies balsamea (L.) Mill.] and black spruce [Picea mariana (Mill.) B.S.P.] forest types in Eastern Canada. Background information to calibrate the model originated from the experimental sites of the ECOLEAP project as well as from a companion study on laboratory soil incubation. Different types of litter were considered in the model: foliage, twigs, understory species, other fine detritus and fine roots. A cohort approach was used to model litter mineralization over time. The soil organic C in the organic (F and H) and mineral layers (0–20 cm) was partitioned into active, slow and passive pools and the rates of C transfer among the different pools and the amount of CO2 respired were modelled. For each forest type, there was a synchrony of response of the C pools to soil temperature variation. The results of the simulations indicated that steady state conditions were obtained under current temperature conditions. When mean annual soil temperatures were gradually increased, the litter and active and slow C pools decreased substantially, but the passive pools were minimally affected. The increase in soil respiration resulting from a gradual increase in temperature was not pronounced in comparison to changes in mineralization rates. An increase in litter production during the same period could contribute to reducing net C losses. Key words: Soil organic matter, litter, soil respiration, climate change
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".