Reevaluation of radiocarbon counting statistics on the MICADAS AMS system: Evidence and implications of non-Poisson distributions on robust uncertainty calculation
Bibliographic record
Abstract
Abstract A reassessment of radiocarbon counting statistics in accelerator mass spectrometry (AMS) at the Andre E. Lalonde National Facility revealed that the traditionally assumed Poisson distribution may not always apply. An extensive analysis of 2.5 years of 14 C and 12 C data was conducted on a MICADAS™ AMS. This study found that only 63% of results adhered to Poisson statistics, while 34.2% showed slight deviations, and 2.8% exhibited strong non-Poisson behavior. This finding challenges the classic assumption that radiocarbon AMS is inherently a Poisson process. This study recommends considering non-Poisson models, specifically quasi-Poisson and negative binomial models, to better account for internal error and improve the accuracy of the reported error. Integrating 12 C current noise into error calculations is also suggested as it plays a significant role in measurement variability. We would like to ignite curiosity on other AMS laboratories to test the non-Poisson error framework with the broader aim of assessing its applicability in improving conventional statistical models, error expansion methods, and in ensuring more accurate and reliable 14 C results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".