Spectral representation: a core aspect of modelling the response characteristics of time-domain EMI mine detectors
Bibliographic record
Abstract
Most modern EMI mine detectors can detect the very small conductive and/or ferromagnetic parts of typical mines with relative ease. However, they also respond significantly to certain soils that contain lossy ferromagnetic minerals. In some special environments such as ocean beaches, conductivity of the host soil may also cause a response. Characterizing and modelling both the various target response mechanisms and the EMI detectors quantitatively would be relatively straightforward if it were not for the fact that most modern EMI detectors operate in time domain and use different current waveforms and time gates to observe response. Furthermore, much of the information about targets and interferences and even instrumental spectral limitations is observational rather than analytical data. In this paper, we put forward a spectral representation method that can be incorporated into both EMI data gathering and instrument modelling and which facilitates efficient quantitative simulation of arbitrary time- domain detection systems. The methodology and examples of its use are presented. Pure induction response from the ground is modelled with a sum-over-N-elements transfer function in which the kernel elements are single pole, pure damping responses which are log-spaced over the spectral range of interest. Instrument transfer functions can be described with a standard sparse pole and zero representation (located anywhere in the damped frequency half plane), if required. Model fitting techniques employing generalized inversion controls are used to go back and forth between frequency and time domain and the set of model parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".