Bibliographic record
Abstract
Controlled‐source electromagnetic prospecting systems which produce vertical current flow in the earth are sensitive to horizontal, electrically resistive structures such as hydrocarbon deposits and fresh water lenses. The electromagnetic response of such three‐dimensional targets can sometimes be approximated by the fields produced by an arrangement of thin resistive sheets buried in a conductive host medium. We have previously shown, through simplified two‐dimensional modeling, that the resistive sheet problem is more subtle than the conductive case commonly used in mineral exploration. Not only does the resistive sheet require the continuity of the normal current density as opposed to the continuity of the tangential electric field, all interaction terms within the coupling matrix must also be calculated in full rather than approximated by point sources representing each element. The purpose of this paper is to extend these results to the more realistic and practical situation of a three‐dimensional resistive sheet buried in a multilayered earth. As an example of the use of the algorithm, the marine controlled‐source electromagnetic response of a simple anticlinal hydrocarbon reservoir underlying near surface resistors such as shallow gas or gas‐hydrate deposits is modeled. Results show that such near surface anomalies, even those with low deflection numbers, significantly affect the response of the deeper target, and must be included in modeling and interpretation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".