Prediction and validation of soil electromagnetic characteristics for application in landmine detection
Bibliographic record
Abstract
Factors controlling the distribution and intensity of soil magnetic susceptibility (MS) and electrical conductivity (EC) were investigated. The purpose was to determine the factors to be considered in predicting MS and EC characteristics of soils in landmine-affected areas and in developing effective landmine detection systems and strategies. Results indicate that knowledge of bedrock geology, soil weathering and transportation (wind and water) history is essential to predict soil MS and EC characteristics. These factors determine the distribution, concentration and mineral type (e.g. ferromagnetic and clay minerals) in soil. For example, fluctuating water tables in tropical climates could produce soils rich in ferromagnetic minerals at the surface, even though their source (bedrock) may have low iron content. Also, subsequent weathering may change these minerals to high or low MS values. Although high clay concentrations homogeneously distributed may not produce high soil EC values, a low clay content concentrated in a single layer may produce extremely high EC values. These suggest that bedrock geology, agricultural soil, air photo and airborne geophysical survey maps can be used for predicting soils MS and EC characteristics of landmine-affected areas. Laboratory and surficial geophysical surveys are techniques for use in validation.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".