Low temperature dependence of electrical resistivity: Implications for near surface geophysical monitoring
Bibliographic record
Abstract
Electrical resistivity imaging surveys are used to monitor variations in pore fluid chemistry and saturation as well as time‐lapse changes. Temperature variations in the near surface can produce larger magnitude changes in electrical conductivity than changes due to slow moving solute plumes or spatial variations in chemistry and soil moisture. Relationships between temperature and electrical conductivity based on previous studies conducted over 25–200°C do not explain 0–25°C laboratory data. A modification to the temperature dependence within a petrophysical model is proposed that may allow general application over this temperature range. An empirical linear approximation of 1.8 to 2.2 percent change in bulk electrical conductivity per degree C is consistent with low temperature electrical conductivity studies and the predictions of the petrophysical model used. This relationship can be used to account for the effect of temperature variations within individual images or time‐lapse difference images.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".