First AID (Atom counting for Isotopic Determination).
Bibliographic record
Abstract
Los Alamos National Laboratory (LANL) has established an in vitro bioassay monitoring program in compliance with the requirements in the Code of Federal Regulations, 10 CFR 835, Occupational Radiation Protection. One aspect of this program involves monitoring plutonium levels in at-risk workers. High-risk workers are monitored using the ultra-sensitive Therrnal Ionization Mass Spectrometry (TIMS) technique to ensure compliance with DOE standards. TIMS is used to measure atom ratios of 239Pua nd 240Puw ith respect to a tracer isotope ('Pu). These ratios are then used to calculate the amount of 239Pu and 240Pup resent. This low-level atom counting technique allows the calculation of the concentration levels of 239Pu and 240Pu in urine for at risk workers. From these concentration levels, dose assessments can be made and worker exposure levels can be monitored. Detection limits for TIMS analysis are on the order of millions of atoms, which translates to activity levels of 150 aCi 239Pua nd 500 aCi for 240Pu. pCi for Our poster presentation will discuss the ultra-sensitive, low-level analytical technique used to measure plutonium isotopes and the data verification methods used for validating isotopic measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.102 |
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".