ABoVE: Soil Moisture and Active Layer Thickness in Alaska, USA and Canada, 2005-2024
Bibliographic record
Abstract
This dataset provides soil thaw depth and moisture measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 529,862 total observations. The dataset includes 224,200 observations of active layer thickness measured by mechanical probing (9.8%) or ground penetrating radar (GPR) (90.2%). Approximately 180,310 volumetric water content (VWC) measurements were collected using GPR (2.0%), HydroSense I and II probes (8.8%), in situ loggers (89.2%), and DualEM (<1.0%). Metadata includes the location, time, geospatial coordinates, sampling technique, measurement teams, and field team contact information. Measurements were typically collected in August and September near the end of the thaw season and cover the period from 2005 to 2024. This dataset, referred to as field measurements of Soil Moisture and Active layer Thickness (SMALT) (Schaefer et al., 2021), was developed for work in Clayton et al. (2021). It has been expanded in Version 2 to comprise a comprehensive dataset of NASA Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign measurements of soil moisture and active layer thickness, including logger data of relevance to Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquisitions. The data are provided in comma separated values (CSV) format.
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 | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".