Modeling electromagnetic fields in the presence of casing
Bibliographic record
Abstract
Electromagnetic (EM) methods in geophysics have wide usability. From mineral and oil exploration Ward and Hohmann (1988); Constable and Cox (1996); Mukherjee and Everett (2011) to deep earth studies Mackie et al. (1993). As a result, large effort has been given to the modeling of electromagnetic phenomena for realistic earth scenarios with the emergence of either staggered grid finite difference techniques Haber et al. (2000); Newman and Commer (2005); Weiss and Newman (2003); Haber and Ascher (2001); Haber (2014) or edge based finite element methods Schwarzbach and Haber (2011); Jin (1993); Key and Ovall (2011) as preferable methods for simulation. Further advances use adaptive mesh Haber and Heldmann (2007); Key and Ovall (2011) in order to obtain better accuracy with fewer cells in the discretization. Presentation Date: Wednesday, October 19, 2016 Start Time: 3:35:00 PM Location: 174 Presentation Type: ORAL
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".