Best practices for using electrical resistivity tomography to investigate permafrost
Bibliographic record
Abstract
Abstract Electrical resistivity tomography (ERT) is a minimally invasive geophysical method that produces a model of subsurface resistivity from a large number of electrical resistance measurements. Strong resistivity contrasts usually exist between frozen and unfrozen earth materials, making ERT an effective and increasingly utilized tool in permafrost research. In this paper, we review more than 300 scientific publications dating from 2000 to 2022 to identify the capabilities and limitations of ERT for permafrost applications. The annual publication rate has increased by a factor of 10 over this period, but several unique challenges remain, and best practices for acquiring, processing, and interpreting ERT data in permafrost environments have not been clearly established. In this paper, we make recommendations for ERT surveys of permafrost and highlight recent advances in the field, with the objective of maximizing the utility of existing and future surveys.
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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 | 0.035 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".