Chemical Sensing of Explosive Targets in the Bedford Basin, Halifax, Nova Scotia
Bibliographic record
Abstract
Sandia National Laboratories has conducted research in chemical sensing and analysis of explosives for many years.Recently, our focus has been on the classification of unexploded ordnance (UXO) in shallow water, unearthed mortar rounds and shells, and anti-personnel/anti tank mines on land by sensing the low-level explosive signatures associated with these objects.The objective of this work is to develop a field portable chemical sensing system that can be used to examine mine-like objects (MLO) and UXO to determine whether there are traces of explosives associated with these objects.A sampling system that can extract explosives from water has been designed and demonstrated previously.This sampler utilizes a flow-through chamber that contains a solid phase microextraction (SPME) fiber to extract and concentrate the explosive molecules.Explosive molecules are then thermally desorbed from the concentrator for rapid desorption into an ion-mobility spectrometer (IMS) for identification.Three variations of this sampling system were evaluated during the Halifax field tests.This chemical sensing system is capable of sub-part-per-billion detection of TNT and related explosive compounds.This paper will describe a demonstration of this system performed in Bedford Basin, Halifax, Nova Scotia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".