Application of laser-induced breakdown spectroscopy for origin assessment of uranium ore refining process intermediates
Bibliographic record
Abstract
The International Atomic Energy Agency (IAEA) has the mandate to safeguard the use of uranium, plutonium and thorium worldwide, as nuclear fuel for civil uses, avoiding their diversion use in weapons of mass destruction or explosive devices. Terrorist and proliferation activists are employing more sophisticated means than those used in the past to achieve their objectives. Border security services, first responders and regulators need to adapt to this challenge and to seek technologies that can provide quick and accurate information, in order to prevent clandestine activities or initiate rapid responses to them. Laser-Induced Breakdown Spectroscopy (LIBS) technique has several advantages, the most relevant are real-time measurement, contact with the sample is not necessary, and analysis can be made at a distance avoiding contamination by radioactive materials. LIBS and Partial Least Square – Discriminative Analysis (PLS DA) were used for the origin assessment of uranium ore refining process intermediates for real-time nuclear forensics. A PLS-DA model was built to assess the origin different process intermediate samples. The results obtained suggest the applicability of LIBS to identify the origin of uranium ore refining intermediate. The correctly classified rate for external validation set is better than 96% for the blind validation set. The results obtained with the transportable LIBS unit clearly show the usefulness of this approach for real-time onsite nuclear forensics.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".