An exploratory study of “large‐ <i>n</i> ” detrital zircon geochronology of the Book Cliffs, <scp>UT</scp> via rapid (3 s/analysis) <scp>U–Pb</scp> dating
Bibliographic record
Abstract
Abstract Detrital zircon (DZ) U–Pb geochronology has improved the way geologists approach questions of sediment provenance and stratigraphic age. However, there is debate about what constitutes an appropriate sample size (i.e., the number of dates in a DZ sample, n ), which depends on project objectives, sample complexity, and, critically, analytical budget. Additionally, there is ongoing concern about bias introduced by zircon grain size. We tested a recently developed rapid (3 s/analysis) data acquisition method by multicollector laser ablation‐inductively coupled plasma‐mass spectrometry (LA‐ICP‐MS) that incorporates an automated selection routine and calculates two‐dimensional grain geometry from polished sample surfaces. Eleven samples were analysed from below and above the Late Cretaceous (Campanian) basal Castlegate unconformity of the Book Cliffs, Utah, in a down‐depositional‐dip transect including Price, Horse, Tusher, and Thompson canyons. 12,448 new concordant dates were generated during two measurement sessions. Results are consistent with recent studies suggesting there is no major provenance change and little time (1–2 Myr) represented across the unconformity. Grain size and sample size both exert a strong control on sample dissimilarity. Age distributions constructed from subsamples of large grains are systematically less similar to whole samples; age distributions composed of small grains are overall more similar to whole samples. As such, North American sediment sources that produce large grains such as the Grenville and Yavapi‐Mazatzal belts can bias age distributions if only large grains are analysed. A sample size of n = 100 is inadequate for characterizing age distributions as complex as those of the Book Cliffs, whereas a sample size of n = 300 provides good characterization. Sample size of n ≈ 1000 or more is unnecessary unless project objectives include scanning for subordinate age groups, such as when identifying the youngest grains for calculating a maximum depositional age (MDA). Dates used in MDA calculations acquired with rapid acquisition are best re‐analysed with longer LA‐ICP‐MS acquisition methods or isotope dilution thermal ionization mass spectrometry for increased accuracy and precision. We include new MATLAB code and open‐source software programs, DZpick and DZmda , for automated spot picking and calculating MDAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".