Assessing The Quality of Electromagnetic Data for The Discrimination of UXO Using Figures of Merit
Bibliographic record
Abstract
Abstract The need for assessing data quality in unexploded ordnance (UXO) remediation problems arises from two sources. In the planning stage it is essential that the data are acquired in sufficient numbers and with sufficient accuracy to answer the detection or discrimination problem of relevance. At the interpretation stage it is critical to objectively assess whether the data are of sufficient quality to warrant subsequent processing, inversion, and classification. Faced with this practical challenge of defining data quality we propose a Figure of Merit (FOM). FOM is a reliability indicator derived from quantities that affect the quality of data, such as anomaly coverage, line spacing, station spacing, instrument noise, survey location errors, etc. The FOM can also include informative features of the inversion, such as the variance of key model parameters, and thus it depends on the inverse model to be applied. Anomalies associated with higher values of FOM should have increased reliability in classification. Anomalies below a critical threshold will not be suitable for advanced analysis. In this paper, we apply the FOM framework to guide the practical interpretation of field data collected at Camp Sibert as part of the Environmental Security Technology Certification Program (ESTCP) Discrimination Study Pilot Project. Using simulations of electromagnetic (EM) data for different quality of survey designs, we examine the success rate of inversions to identify key FOM parameters that can explain unreliable inversion results. In this manner, the relationship between FOM and reliability is calibrated on synthetic data before application to the interpretation of field data. A trust index for each inversion can subsequently be included into a discrimination algorithm to help establish a priority dig list. We find that incorporating the FOM in the classification procedure significantly reduces the number of non-UXO items that need to be excavated to recover all UXO.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".