Fine scale assessment of seasonal, intra-seasonal and spatial dynamics of soil CO2 effluxes over a balsam fir-dominated perhumid boreal landscape
Bibliographic record
Abstract
Total soil CO2 efflux (FCO2) is the second most important carbon flux after photosynthesis in boreal forests. However, accurate modelling of FCO2 remains challenging because of its high variability, both temporally and spatially. Using a Abies balsamea-dominated boreal landscape in Quebec (eastern Canada) as a case study, we modelled seasonal, intra-seasonal and spatial variability of FCO2 using climate variables and topographic and canopy structure attributes derived from Light Detection and Ranging (LiDAR) and assessed their respective contributions to soil CO2 emissions. Weekly point measurements of FCO2 at 99 sites were taken over an area of 122 ha between June and October 2020. The seasonal component of FCO2 was quantified and subtracted from FCO2 measurements to isolate the spatial and intra-seasonal components of the flux. The two components were then modelled using a Random forest regression model and studied using accumulated local effect plots (ALE plots). Our approach allowed to explain 79% of the variation in FCO2: the seasonal pattern explained 35% of the variation in FCO2 measurements, while spatial and intra-seasonal patterns together explained 44%. The most important factors explaining spatial variation were vegetation height and the topographical convergence index[AH1] . Average air temperature of the last two days before efflux measurements was the most important factor explaining intra-seasonal variation. The proposed methodology makes it possible to predict FCO2from external factors derived from climate and remote sensing data and enables the decomposition of FCO2 into its seasonal, intra-seasonal and spatial components. Our results demonstrate the importance of spatial and intra-seasonal variations in FCO2 compared to seasonal variation, a finding that has implications for the measurement and modelling of FCO2 at landscape and global scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".