Laser spectroscopy applied in radiocarbon dating with the bomb peak
Bibliographic record
Abstract
Bomb-peak dating plays a crucial role in forensic applications. By comparing the radiocarbon concentrations of samples containing biological carbon with the bomb-peak curve, their ages can be accurately determined. Accelerator mass spectroscopy (AMS) is currently the most advanced technique for radiocarbon analysis; however, it is hindered by high costs, complex construction, maintenance requirements, and labor-intensive sample preparation. In contrast, S aturated absorption CA vity R ing-down (SCAR) spectroscopy has merged as an innovative, cost-effective, and efficient alternative for radiocarbon analysis. This technique has already demonstrated its competitiveness in biofraction analysis. In this study, we report, for the first time, the application of SCAR spectroscopy in bomb-peak dating. Unlike AMS, SCAR spectroscopy directly measures the concentration of radiocarbon by analyzing the absorption spectrum of 14 CO 2 gas, eliminating the need for complex graphitization during sample preparation. To evaluate the feasibility of this technique in dating applications, we analyzed various types of samples, including wine, paper, and wood. The radiocarbon ages obtained using SCAR spectroscopy showed reasonable consistency with the age information of these samples, demonstrating its potential as a reliable tool for bomb-peak dating.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".