Enhanced U-Pb detrital zircon, Lu-Hf zircon, δ18O zircon, and Sm-Nd whole rock global databases
Bibliographic record
Abstract
Abstract High-quality global isotopic databases provide Earth scientists with robust means for developing and testing a variety of geological hypotheses. Database design establishes the range of questions that can be addressed, and validation techniques can enhance data quality. Here, six validated global isotopic databases provide extensive records of analyses from U-Pb in detrital zircon, Lu-Hf in zircon, Sm-Nd from whole rocks, and δ18O in zircon. The U-Pb detrital zircon records are segregated into three independently sampled databases. Independent samples are critical for testing the replicability of results, a key requisite for gaining confidence in the validity of a hypothesis. An advantage of our updated databases is that a hypothesis developed from one of the global detrital zircon databases can be immediately tested with the other two independent detrital zircon databases to assess the replicability of results. The independent εHf(t) and εNd(t) values provide similar means of testing for replicable results. This contribution discusses database design, data limitations, and validation techniques used to ensure the data are optimal for subsequent geological investigations.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".