Bibliographic record
Abstract
Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks, turnover times, and soil texture. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modelling at local, regional or global scales. This paper presents this dataset, describes in detail soil sampling, laboratory analysis and derived soil geochemical parameters, calculations and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates within the soil area of the northern circumpolar permafrost region (17.9 × 106 km2) using the ESA’s Climate Change Initiative (CCI) Global Land Cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0–100 cm and 0–300 cm soil depth to 380 Pg and 813 Pg for carbon and 21 Pg and 55 Pg for nitrogen, respectively. Of which 48 % of the area is within the land cover class forest with a total SOC and TN storage for 0–300 cm of 35 % and 36 %, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (−170 to +186 Pg) to 300 cm depth but show different spatial patterns. Two separate datasets are freely available on the Bolin Centre Database repository (https://doi.org/10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a and https://doi.org/10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.034 | 0.015 |
| Insufficient payload (model declined to judge) | 0.076 | 0.066 |
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".