Accelerator mass spectrometry: an analytical tool with applications for a sustainable society
Bibliographic record
Abstract
Abstract Accelerator Mass Spectrometry (AMS) adds the techniques of higher energy charged particle acceleration to the basic principles of Isotope Ratio Mass Spectrometry (IRMS) to provide extremely low detection capability (below 1 femtogram) of rare isotopes in samples of natural materials as small as 1 mg. Depending on the element selected and the configuration of the equipment, rare isotope sensitivities can reach less than one part in 1015. The advantages of this small sample size and high sensitivity for the detection of rare isotopes include a) the economic benefit of collecting, shipping and preparing much smaller samples, and b) the ability to analyse specific chemical compounds within the sample. For the latter advantage, the pathway taken by that compound through a complex system can be more precisely traced or, in the case of radioactive isotopes, more precise chronological information can be provided. The paper is an amplification of material which was presented at the IAEA International Conference on Accelerators for Research and Sustainable Development: novel concepts and technical innovation. It begins with a basic overview of AMS technology, with an emphasis on how the use of higher energy contributes to this enhanced sensitivity, and then provides several examples of new AMS technologies which reduce the energy and space requirements for such systems. Several examples of applications which contribute to the investigation of sustainability in other areas of environmental concern are then briefly described.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".