The Utility of a Fully-distributed Direct Current Resistivity and Induced Polarisation System with Common Voltage Referencing
Bibliographic record
Abstract
SummaryThe direct current electrical resistivity and induced polarization (DCIP) method has received another significant upgrade through the introduction of common voltage referencing (CVR) in a fully-distributed array system. An array of single-channel receivers with a CVR wire allows for the extraction of an unprecedented volume of dipole data for the number of receivers deployed. In 3D implementation, this new method reduces noise levels and allows for the derivation of multi-scale and multi-azimuth receiver dipoles.Operational efficiencies in the CVR method include lower overall wire lengths, less equipment weight and less crew fatigue when compared with conventional and other distributed array methods. Cable-free mesh network capability in each receiver allows for real-time assessment of data quality metrics, safety information, location data, and system health data. These operational efficiencies translate directly to improvements in safety.With several hundred active receivers, data volume can reach 10s of millions of data records. Careful processing and selection of an optimised data subset with multi-scale and multi-azimuth information will inform highly accurate inversion imaging.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".