Large-scale assessment of permafrost conditions using the Canadian Permafrost Electrical Resistivity Survey (CPERS) database
Bibliographic record
Abstract
Electrical resistivity tomography (ERT) is a geophysical technique that is commonly used to investigate permafrost conditions because the resistivity of earth materials tends to increase greatly when they are frozen, particularly if they are ice-rich.Despite the increasingly widespread use of ERT for permafrost applications over the last 20 years, data sharing in Canada and most other countries has been limited.We created the Canadian Permafrost Electrical Resistivity Survey (CPERS) database as a platform for standardized and accessible sharing of historical and current ERT datasets collected in permafrost environments.Individual researchers from several Canadian institutions have already contributed 280 ERT datasets and associated descriptive, standardized metadata.These datasets were collected between 2008 and 2022 from sites in British Columbia, Labrador, Northwest Territories, Québec, and Yukon, as well as Alaska.Here, we used the published datasets to examine relationships between permafrost resistivity, climate data, and site conditions, including landform type, disturbance, and nearsurface substrate.The findings show an inverse relationship between mean annual air temperature and permafrost resistivity, with variability controlled by site conditions.These analyses demonstrate the utility of the CPERS database for examining largescale trends in permafrost conditions across northern North America, a usefulness that will increase in the future as additional datasets are incorporated.1
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".