Standoff high energy laser induced oxidation spectroscopy (HELIOS)
Bibliographic record
Abstract
High Energy Lasers (HELs) used for defense applications require operational distances ranging from few hundred meters to several kilometers. As the distance increases, the incident beam properties and, consequently, the anticipated effect delivered to the sample become less predictable. Therefore, the direct observation of the event induced by the laser can become an asset. In this paper, we propose a novel spectroscopic method that analyses in real time the spectral components present in the flames produced during the interaction of a HEL with a metallic piece at a long distance. This method was used on aluminum and carbon steel samples placed 200 m away from the laser system. It was discovered that the aluminum and iron oxides created as a by-product of the HEL reaction with the samples emitted clear fingerprint signatures that could be detected remotely using a spectroscopic receiver placed beside the HEL beam director. The real-time assessment of the laser-induced effect can be achieved by monitoring the temporal evolution of the oxide signatures, hence providing information to the operator about the reaction and the nature of the sample illuminated.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".