Application of frequency domain induction EM soundings with controlled source (FDEMS method) for precise tracing of boundaries in geoelectrical sections
Bibliographic record
Abstract
The FDEMS method was introduced in the former USSR at the turn of the 50s and 60s of the last century as an integral part of the triad of induction EM methods (MT, FDEMS, TDEM), which were actively developed in the 50s after the grand discoveries by A.N. Tikhonov and L. Cagniard. The method was not widely used, primarily due to lack of suitable hardware and software for data processing and interpretation. Nevertheless, FDEMS was actively developed in certain regions of Russia and Ukraine until the present days. Interest in the method is supported by the potentially high accuracy of mapping high-resistivity boundaries, since in the FDEMS method there is a direct relationship between the ratio (R / H) of the sounding spacing (R) to the depth (H) to the high-resistivity reference horizon pronounced by significant points of amplitude and phase frequency characteristics (curves). A number of successful FDEMS surveys were completed on the Ukrainian Shield and its slopes, Dnipro-Donetsk basin (Ukraine) and different parts of Russia and Uzbekistan which achieved positive results (1977-2000). To date, the capabilities of modern multifunction and multichannel equipment, software for processing and interpreting field data allows to realize to a large extent the prospective capabilities of the FDEMS method for high-precision mapping of boundaries in the geoelectric section and mapping of low-contrast objects.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".