Recommendations for the reporting and interpretation of isotope dilution U-Pb geochronological information
Bibliographic record
Abstract
Abstract U-Pb geochronology by isotope dilution–thermal ionization mass spectrometry (ID-TIMS) has the potential to be the most precise and accurate of the deep time chronometers, especially when applied to high-U minerals such as zircon. Continued analytical improvements have made this technique capable of regularly achieving better than 0.1% precision and accuracy of dates from commonly occurring high-U minerals across a wide range of geological ages and settings. To help maximize the long-term utility of published results, we present and discuss some recommendations for reporting ID-TIMS U-Pb geochronological data and associated metadata in accordance with accepted principles of data management. Further, given that the accuracy of reported ages typically depends on the interpretation applied to a set of individual dates, we discuss strategies for data interpretation. We anticipate that this paper will serve as an instructive guide for geologists who are publishing ID-TIMS U-Pb data, for laboratories generating the data, the wider geoscience community who use such data, and also editors of journals who wish to be informed about community standards. Combined, our recommendations should increase the utility, veracity, versatility, and “half-life” of ID-TIMS U-Pb geochronological 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 | 0.175 | 0.514 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.049 |
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".