Bibliographic record
Abstract
Abstract. The carbon cycle in Arctic-boreal regions (ABR) is an important component of the planetary carbon balance, with growing concerns about the consequences of ABR warming on the global climate system. The greatest uncertainty in annual carbon dioxide (CO2) budgets exists during the non-growing season, primarily due to challenges with data availability and limited spatial coverage in measurements. The goal of this study was to determine the main environmental controls of non-growing season CO2 fluxes in ABR over a latitudinal gradient (45° N to 69° N) featuring four different ecosystem types: closed-crown coniferous boreal forest, open-crown coniferous boreal forest, erect-shrub tundra, and prostrate-shrub tundra. CO2 fluxes calculated using a snowpack diffusion gradient method (n = 560) ranged from 0 to 1.05 gC m2 day-1. To assess the dominant environmental controls governing CO2 fluxes, a Random Forest machine learning approach was used. We identified that soil temperature as the main control of non-growing season CO2 fluxes with 68 % of relative model importance, except when soil liquid water occurred during zero degree Celsius curtain conditions (Tsoil ≈ 0 °C and liquid water coexists with ice in soil pores). Under zero-curtain conditions, liquid water content became the main control of CO2 fluxes with 87 % of relative model importance. We observed exponential regressions between CO2 fluxes and soil temperature (RMSE = 0.024 gC m-2 day-1) in frozen soils, as well as liquid water content (RMSE = 0.137 gC m-2 day-1) in zero-curtain conditions. This study is showing the role of several variables on the spatio-temporal variability of CO2 fluxes in ABR during the non-growing season and highlight that the complex vegetation-snow-soil interactions in northern environments must be considered when studying what drives the spatial variability of soil carbon emission during the non-growing season.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.218 | 0.156 |
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".