Live-Site Discrimination Analysis of Polarization Tensor Parameters Extracted from Time-Domain Sensors
Bibliographic record
Abstract
Abstract In this paper, we compare the discrimination potential of feature vectors extracted from polarization tensor fits to two types of time-domain electromagnetic data collected at two live-sites: a Geonics EM61 metal detector with 4 time-channels and a Geonics EM63 with 26 channels spanning a longer time-range. Pervasive on the first live-site were non-ferrous adapters, which comprised almost 40% of the items excavated. The discrimination challenge was to identify larger ferrous unexploded ordnance and smaller 40 mm grenades while minimizing excavations of adapters. For the EM61, the relative size and decay rate of the primary and secondary polarizations allowed many adapters to be excluded from the dig list. The standard deviation of the magnetic field data in a 0.5-m radius around the center of the anomaly was also a highly effective feature for discriminating against adapters. For the EM63, the decay of the secondary polarization of the adapters was significantly different than that of any of the UXO. Consequently, the derived polarization parameters were very effective in discriminating both UXO and 40-mm grenades from the adapters. The longer measurement time of the EM63 resulted in superior discrimination performance to the EM61 and obviated the need for supplemental magnetic data. At the second live-site we compare EM63 datasets collected in both detection and cued-interrogation modes. The primary and secondary polarizations of 37-mm projectiles and MK-23 practice bombs found at the site were more tightly clustered for the cued-interrogation data and agreed closely with previously derived test-stand values. In addition, the secondary and tertiary polarizations were in close agreement for the radially-symmetric ordnance, so that a feature related to the difference of these polarizations has good discrimination potential. This was not the case for the discrimination mode data, where there were often large differences between the secondary and tertiary polarizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".