Near-surface electromagnetic induction — Introduction
Bibliographic record
Abstract
There has been explosive growth in near-surface electromagnetic (EM) induction geophysics in the past several years (Everett, 2012). New and experienced practitioners are achieving great success in applying the method to an increasing variety of problems. Moreover, theorists are becoming better able to exploit the rich information content that is available in electromagnetic induction data sets. The EM induction method, with its broad opportunities to design new transmitters, receivers, and interpretation tools, continues to offer wide avenues to capture the spatial complexity of the subsurface. This special Geophysics issue brings forward the latest achievements, which should stimulate interest across a broad spectrum of geophysicists, as well as set the tone for continuing developments in this field. This special issue contains advances in theory, instrumentation, data processing and interpretation, and innovative applications of near-surface applied EM induction geophysics. The range of topics is varied and, as described below, includes modeling and inversion, airborne electromagnetics, hydrogeophysics, soil science, audiomagnetotellurics, unexploded ordnance (UXO) discrimination, archaeology, geothermal mapping, and joint inversion of EM and magnetic resonance sounding data. Vrbancich analyzes helicopter time-domain EM data acquired over shallow seawater overlying reefs and sediment-filled paleovalleys. A favorable comparison is shown of water depths and sediment thickness derived from 1D inversion with known bathymetry and independent marine seismic …
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".