Hot spots and hot moments drive (shape) spatiotemporal variations of greenhouse gas fluxes from montane forests 
Bibliographic record
Abstract
Temperate forest soils are considered as significant sources or sinks for carbon dioxide, methane and nitrous oxide. There are relatively few studies which specifically deal with soil greenhouse gas (GHG) fluxes across slope positions in upland temperate forest. We used static chambers to monitor soil GHG fluxes at three slope position sites (top, middle and bottom) in the Pinus tabulaeformis forest in the Qinling Mountains, China from July 2012 to June 2015. The cumulative soil CO2 and N2O emissions and the CH4 uptake from the three sites ranged between 13.85 to 14.49 t CO2 ha-1 yr-1, 1.04 to 6.48 kg N2O ha-1 yr-1, and 4.78 to 9.01kg CH4 ha-1 year-1, respectively. There were large pulses of CO2 emissions during spring and summer after heavy rainfall or during freezing and thawing cycles. The proportion of the annual flux of CO2 during the 2014 summer could achieve 45.6%, 49.6% and 43.5% at the three sites and during 2013 summer were more than 36% at the bottom and middle positions. At the bottom and middle positions, the proportions of the annual flux of CO2 were more than 33% during the majority of the summer periods. Soil microenvironment (soil moisture, soil temperature and the interaction of these two factors) explained 84.96% variations of CO2 emissions. The emission summits of 10547.1 ug CH4 m-2 h-1, 6256.5 ug CH4 m-2 h-1 and 701.5 ug CH4 m-2 h-1 were observed after the first heavy rainfall in the spring of 2013 at the three sites. The soil of the middle slope position even acted as a year round net weak source of CH4 due to continuous rainfall and mixed with heavy rainfall during the 2014 summer. Soil microenvironment and accumulated rainfall in seven anteceding days (rain_7) explained 31.38% variations of CH4 fluxes. The proportion of the annual flux of N2O that is derived from the 2015 and 2014 freeze-thaw cycles could achieve 38% and 30.6 % at the bottom and top slope positions. Soil microenvironment and rain_7 explained 56.67% variations of N2O fluxes. Due to hot moments and hot spots effects, the contribution of N2O emission to the total global warming potential was much higher than the cutting down effects of CH4 consumption during 2012-2013 or at the middle position site. Our study demonstrated that the middle and bottom position sites were hot spots and the rainfall events during the growing season were triggers of hot moments for all three greenhouse gases. The hot moments and hot spots of the three greenhouse gases are a significant fraction of their total budgets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".