Predicting Peatland Net Ecosystem Exchange of CO2 during the Non-Growing Season using Machine Learning
Bibliographic record
Abstract
Abstract The world’s cold regions are experiencing some of the fastest warming, especially during the winter and shoulder seasons. Recent studies have highlighted the significance of carbon dioxide (CO2) emissions during the non-growing season (NGS) to the annual carbon budgets of northern peatlands. Because of the positive feedback of soil microbial respiration to warming, a warmer NGS may be expected to alter the carbon balance of peatlands, which are estimated to store about one-third of global terrestrial organic carbon stocks. However, estimates of NGS net ecosystem CO2 exchange (NEE) remain highly uncertain. In this study, we determine key environmental variables affecting the NGS-NEE from a temperate peatland (Mer Bleue Bog; Ottawa, Canada) and predict future NGS-NEE under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5) using a variable selection methodology, global sensitivity analysis, and data-driven model. The model successfully reproduces the observed NGS-NEE fluxes using only 7 variables, with NGS-NEE being most sensitive to changes in net radiation. Our projections estimate that mean NEE during the NGS could increase by up to 103% by the end of the 21st century; thus, reinforcing the urgent need for a comprehensive understanding of peatlands as evolving sources of atmospheric CO2 in a warming world.
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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 | 0.000 |
| 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".