Soil organic carbon, active layer depth and visible ground ice content for Herschel Island (Qikiqtaruk)
Bibliographic record
Abstract
This dataset contains soil pedon data collected in 2015 on Herschel Island (Qikiqtaruk; 69°34′N, 138°55′W), Beaufort Sea, Canada. Large amounts of soil organic carbon (SOC) are stored in high-latitude ecosystems. Knowledge on storage and variability provides essential base information to asses the vulnerability of this SOC. In total 38 soil pedons were sampled along transects in three typical tundra terrain types common across the Arctic region. These terrain types (unit) are hummocky tussock upland tundra (HT-Tundra), non-sorted circles on upland tundra (NSC-Tundra) and ice-wedge polygon terrain (IWP-Tundra). A main transect was set up with increasing sampling distance from the center and complemented with a shorter crossing transect. This aimed to maximize the distances between individual soil pedons in order to test geostatistical properties of SOC variability. At each pedon location a 1 m wide soil pit was sampled and described using a soil horizon oriented approach. The soil pit was then divided in 10 cm intervals generating 11 sub-pedons. SOC storage was calculated for specific depth intervals SOC 0–30 cm, SOC 0–100 cm, SOCAL (SOC in active layer), SOCPF (SOC in permafrost), as well as the visible ice content for the permafrost section (mean_vis_ice). Additional data includes the depth of the permafrost table (depthPF) and the location of the sub-pedon within the periglacial landforms they occupy (landformLoc). For the active layer, the data was calculated based on perspective corrected images of the soil pit, while for the permafrost section homogeneous layering was applied.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".