Creating a database of electrical resistivity tomography surveys of permafrost in Canada and establishing best practices for data processing and sharing
Bibliographic record
Abstract
Electrical resistivity tomography (ERT) is a geophysical method that produces an estimate of subsurface resistivity distribution, which can be used to infer the presence and extent of frozen ground. Repeated ERT surveys indicate how subsurface temperature and ground ice conditions are changing over time, which is particularly important for evaluating the changes and risks associated with climate change. However, there is no existing framework for sharing ERT data and datasets are rarely published, making it difficult to find and use historical data to assess subsurface changes. To facilitate data sharing, we are developing a Canadian database for ERT surveys of permafrost. A key component of this project is the development of an automated ERT data processing workflow to prepare datasets. Establishing best practices for data processing ensures that ERT results are optimized and standardized, which is essential so that changes in subsurface conditions can be reasonably interpreted. We also present our web-based data visualization tool that allows for targeted searching of surveys and plotting of selected results. By storing ERT data in a standardized and accessible way, our goal is to facilitate interpretations of permafrost change on a range of spatial and temporal scales and guide future research in permafrost science.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.031 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".