Ghost age components in detrital thermochronology
Bibliographic record
Abstract
Thermochronological dating of detrital samples is an important tool for understanding the thermal history of basins and their source regions. Detrital thermochronology data are often complex with apparent ages ranging over hundreds of million years due to variations in source-rock cooling age and rate, and the influence of post-depositional burial. A common strategy to interpret such data is to split a dataset into several age components by finite-mixture modeling. Herein, we describe for the first time a remarkable pattern of age components in some thermochronological datasets: two or more components showing ages that are approximately multiples of each other. We apply finite-mixture modeling to a log-normal age distribution (a random effects model) and show that these “ ghost age components” are artifacts of inappropriately fitting discrete components to continuous age ranges. The striking pattern of the age components is a direct consequence of the underlying age distribution. For a continuous log-normal age distribution, the ghost components have approximately similar age ratios that depend on the overdispersion and the number of components. Due to their artifactual nature, ghost components appear in a range of dating methods, including fission-track, U Pb and (U Th)/He dating, and in equivalent doses of luminescence dating. As they are not geologically meaningful, their occurrence hampers the geological interpretation of thermochronological data. We thus propose to carefully decide for the number of fitted components based on grain properties and additional geological information and to screen modeling results for the constant age ratios that are characteristic of ghost components.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".