Examples of the Effect of Magnetic Soil Environments on Time Domain Electromagnetic Data
Bibliographic record
Abstract
During September 2004 a field study was carried out on the Hawaiian Island of Kaho'olawe to explore various aspects of the effect of magnetic soils on time domain electromagnetic (TEM) measurements. This field work was in support of two Strategic Environmental Research and Development Program funded research projects (UX1355 and UX1414) whose goals are to investigate the source and spatial variability of magnetic soil anomalies, to create a methodology for modelling the response of magnetic soils, and to develop TEM data collection techniques that can better discriminate between the response of magnetic soils and unexploded ordnance (UXO). Detailed electromagnetic surveys were carried out at a test site on the island. The data from the surveys verify the commonly held belief that magnetic variations in the soil can complicate the identification of UXO. However, the data also show that short wavelength variations in the TEM response due to micro-topographic variations and coil orientation effects can generate responses that could mask a UXO and/or result in a false positive. An overview of the surveys, a discussion of preliminary results and some practical recommendations for surveying in magnetic soil environments will be presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".