(U-Th)/He chronology: Part 1. Data, uncertainty, and reporting
Bibliographic record
Abstract
Abstract The field of (U-Th)/He geochronology and thermochronology has grown enormously over the past ∼25 years. The tool is applicable across much of geologic time, new (U-Th)/He chronometers are under continuous development, and the method is used in a diverse array of studies. Consequently, the technique has a rapidly expanding user base, and new labs are being established worldwide. This presents both opportunities and challenges. Currently there are no universally agreedupon protocols for reporting measured (U-Th)/He data or data derivatives. Nor are there standardized practices for reporting He diffusion kinetic, 4He/3He, or continuous ramped heating data. Approaches for reporting uncertainties associated with all types of data also vary widely. Here, we address these issues. We review the fundamentals of the methods, the types of materials that can be dated, how data are acquired, the process and choices associated with data reduction, and make recommendations for data and uncertainty reporting. We advocate that both the primary measured and derived data be reported, along with statements of assumptions, appropriate references, and clear descriptions of the methods used to compute derived data from measured values. The adoption of more comprehensive and uniform approaches to data and uncertainty reporting will enable data to be re-reduced in the future with different interpretative contexts and data reduction methods, and will facilitate inter-comparison of data sets generated by different laboratories. Together, this will enhance the value, cross-disciplinary use, reliability, and ongoing development of (U-Th)/He chronology.
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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 | 0.088 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".