Monitoring changes in unfrozen water content with electrical resistivity surveys in cold continuous permafrost
Bibliographic record
Abstract
For permafrost, information on unfrozen water content is critical for thermal modelling, understanding permafrost evolution and for predicting thaw susceptibility for infrastructure. We utilize electrical resistivity surveys for the detection and monitoring of unfrozen water content in cold continuous permafrost. We develop a data processing scheme based on a common subset of reciprocal measurements to filter data from irregular acquisitions in variable environmental conditions to a standard noise level, such that models recovered from disparate data sets can be compared quantitatively. Using temperature-corrected post-inversion model differencing, changes in recovered electrical resistivity models are used to calculate the relative change in unfrozen water content over common regions of high model resolution. The technique is applied to electrical resistivity data collected seasonally using a permanent electrode installation in cold continuous permafrost alongside airport infrastructure in the Canadian Arctic. The number of healthy data decreases over the experiment duration, with a correlation between data quality and ground surface temperature. Using the resistivity models, relative changes in unfrozen water content are estimated that are consistent with temperature records. Results are compared to an empirical temperature-dependent water content model obtained via calibrated thermal modelling at the study site. When compared to the empirical model, the changes in resistivity overpredict the changes in unfrozen water content due to temperature-induced phase changes alone after one year of acquisition. Prevalent changes in unfrozen water content occur in a zone below the active layer at temperatures of approximately 0 to −4.5 °C, and may be indicative of permafrost evolution involving ice formation or water movement over many months in moderately-saline permafrost beneath airport infrastructure.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".