CAUM: A software for calculating and assessing chemical ages of uranium minerals
Bibliographic record
Abstract
• CAUM is a new, freely available, user-friendly software that can be used to calculate the chemical age of uranium minerals. • With friendly interfaces, CAUM is easy to input files (.xlsx), output results and results visualization. • The convenience and effectiveness of CAUM is demonstrated by examples using published data from the literature. It has been shown that the age of minerals in which U ± Th are a major (e.g., uraninite, pitchblende and thorite) or minor (e.g., monazite, xenotime) component can be calculated from the concentrations of U ± Th and Pb rather than their isotopes, and such ages are referred to as chemical ages. Although equations for calculating the chemical ages have been well established and various computation programs have been reported, there is a lack of software that can not only calculate the chemical ages of individual analytical points but also provide an evaluation of the errors of individual ages as well as the whole dataset. In this paper, we develop a software for calculating and assessing the chemical ages of uranium minerals (CAUM), an open-source Python-based program with a friendly Graphical User Interface (GUI). Electron probe microanalysis (EPMA) data of uranium minerals are first imported from Excel files and used to calculate the chemical ages and associated errors of individual analytical points. The age data are then visualized to aid evaluating if the dataset comprises one or multiple populations and whether or not there are meaningful correlations between the chemical ages and impurities. Actions can then be taken to evaluate the errors within individual populations and the significance of the correlations. The use of the software is demonstrated with examples from published data.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".