Towards the first circumarctic N2O budget – Extrapolating to the landscape scale
Bibliographic record
Abstract
The Arctic is warming at twice the rate of the rest of the globe. While it has been increasingly highlighted that thawing permafrost accelerates soil organic matter decomposition, research on biogeochemical N cycling is still underrepresented. Arctic nitrous oxide (N2O) emissions have long been assumed to have a negligible climatic impact but recently increasing evidence has emerged of N2O hotspots in the Arctic. Even in small amounts, N2O has the potential to contribute to climate change due to it being nearly 300 times more potent at radiative forcing than CO2. Therefore, the ‘NOCA’ project aims to establish the first circumarctic N2O budget. Following intensive N2O flux sampling campaigns at primary sites within Northern Russia and soil N2O concentration measurements from secondary sites across the Arctic, we are now entering the phase of spatial extrapolation. Challenges to overcome are the small-scale heterogeneity of the landscape and incorporating small features that can function as N2O hotspots. Therefore, as a first step in upscaling the N2O fluxes, high resolution imagery is needed. We show here novel high-resolution 3D imagery from an unmanned aerial vehicle (UAV), which will be used to upscale N2O fluxes from plot to landscape scale by linking ground-truth N2O measurements to vegetation maps. This approach will first be applied to the East cliff of Kurungnakh Island in the Lena River Delta of North Siberia and is based on 2019 sampling campaign data. Kurungnakh Island is characterized by ice- and organic-rich Yedoma permafrost that is thawed by fluvial thermo-erosion forming retrogressive thaw slumps in various stages of activity. Overall, 20 sites were sampled along the cliff and inland, covering the significant topographic and vegetative characteristics of the landscape. The data from this scale will provide the basis for extrapolating, by using a stepwise upscaling approach, to the regional and finally circumarctic scale, allowing a first rough estimate of the current climate impact of N2O emissions from permafrost affected soils. Available international circumarctic data from this and past projects will be synthesized with an Arctic N2O database under development for use in future ecosystem and process-based climate model simulations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".