VizualAge: A Novel Approach to Laser Ablation ICP‐MS U‐Pb Geochronology Data Reduction
Bibliographic record
Abstract
VizualAge, a new computer software tool for analysing U‐Pb data obtained by laser ablation‐inductively coupled plasma‐mass spectrometry, was developed. It consists of a data reduction scheme (DRS) for Iolite (a general mass spectrometry data analysis tool) as well as visualisation routines. In addition to the U/Pb and Th/Pb ages calculated by Iolite’s U‐Pb geochronology DRS, VizualAge also calculates 207 Pb/ 206 Pb ages and common Pb corrections for each time‐slice of raw data. Importantly, VizualAge allows one to display a live concordia diagram for visualising data on such a diagram as an integration interval is being adjusted. This provides instantaneous feedback regarding discordance, uncertainty, error correlation and common Pb. Several zircon data sets were used to illustrate how the live concordia could be used as a powerful inspection tool, revealing a single analysis to consist of zones of concordance, metamict areas, as well as inherited cores or younger overgrowths. VizualAge also constructs histograms, conventional and Tera‐Wasserburg type concordia diagrams, as well as 3D U‐Th‐Pb and total U‐Pb concordia diagrams. The precision and accuracy of data reduced with VizualAge are demonstrated with examples of the Plešovice, Temora‐2 and Penglai zircon reference materials. Data for zircon from the Long Lake Batholith (Wyoming craton) were used to illustrate how VizualAge calculated common Pb corrections and helped to expose as yet unexplained difficulties with accurately determining 204 Pb.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".