Predicting Non-Growing Season Net Ecosystem Exchanges of CO2 from a Canadian Peatland 
Bibliographic record
Abstract
The world’s cold regions are experiencing some of the fastest warming, especially during the winter and shoulder seasons. Recent studies have further highlighted the significance of carbon dioxide (CO2) emissions during the non-growing season (NGS) to the annual carbon (C) budgets of northern peatlands. Because of the positive feedback of soil microbial respiration to warming, even at sub-zero temperatures, a warmer NGS may be expected to alter the C balance of peatlands, which are estimated to store about one-third of global terrestrial organic C stocks. However, estimates of NGS net ecosystem CO2 exchange (NEE) of peatlands remain highly uncertain. In this study, we use a variable selection methodology and a global sensitivity analysis (GSA) to determine the most influential environmental variables affecting the NGS-NEE of CO2 in a temperate Canadian peatland (Mer Bleue Bog; Ottawa, Canada). A data-driven machine learning model is trained on a 13-year (1998-2010) continuous record of eddy covariance flux measurements at the site. The model successfully reproduces the observed NGS-NEE CO2 fluxes using only 7 variables: soil temperature, soil moisture, air temperature, wind direction and speed, net radiation, and upwelling photosynthetic photon flux density. Of these 7 input variables, NGS-NEE is most sensitive to changes in net radiation, likely through the latter’s strong linkages to variations in plant phenology and snow cover. We further predict how the future NGS-NEE of the Mer Bleue Bog will change under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5). According to the projections, mean NEE during the NGS could increase by up to 103% by the end of the 21st century. Our results thus reinforce 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 | 0.001 | 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 | 0.001 |
| 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".