Terrain influence on soil organic carbon and total nitrogen sorage in soils of Herschel Island
Bibliographic record
Abstract
The Arctic-wide increase of permafrost temperatures and subsequent thaw is mobilising large amounts of organic matter that is stored in permafrost environments. Organic matter decomposition results in the release of carbon dioxide and methane, which will amplify the warming and will cause so called permafrost carbon feedback. Increasing air temperatures due to greenhouse gas emissions from permafrost is not yet incorporated into Earth System Models. The lack of high-resolution carbon storage data and factors influencing it are two important uncertainties hindering modelling efforts. \nIn this study we estimate soil organic carbon (SOC) and total nitrogen (TN) storage on Herschel Island and we identify the effect of terrain properties on SOC and TN storage. Herschel Island is characterised by diverse terrain and the occurrence of mass movements. We analysed 128 active layer and permafrost samples from 11 cores and pits for SOC and TN contents and extrapolated them to ecological units. These ecological units were generated from multispectral remote sensed imagery on the basis of soil and vegetation ground surveys. The average estimated SOC and TN storage for Herschel Island is 34.8 kg C m-2 and 3.4 kg N m-2. This high SOC and TN storage is in the range of other studies conducted in the western Canadian Arctic and Alaska. \nSOC storage showed high positive correlation with topographic wetness index which is an indicator of catenary position and slope characteristics. Comparison of SOC storage between the study sites showed statistically significant different storage between three groups: 1) undisturbed uplands, 2) mass wasting sites (occurrence of solifluction and past active-layer detachment), and 3) accumulation sites (peatlands and alluvial fans). The same groups showed also different down-core SOC, TN and dry bulk density trends. Undisturbed uplands stored the majority of SOC in the upper part of the profile, which was decreasing with depth together with higher ground-ice contents. Mass wasting sites showed depleted storage in the upper 50 cm and slightly increased storage with depth due to material compaction. Accumulation sites showed high storage throughout whole profile. In conclusion, our results indicate that terrain has an important influence on SOC storage. SOC and TN stocks are highest in accumulation environments and lowest on sites where mass wasting occurs. \n
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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 | 0.000 | 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) | 0.001 | 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".