Geochronology of the Early Triassic based on coupled Bayesian zircon eruption age and Bayesian age–depth models
Bibliographic record
Abstract
Precise and accurate geochronology is essential for reconstructing Earth's history and coeval life evolution. The Early Triassic was a critical time interval following the greatest Phanerozoic mass extinction, recording remarkable biotic changes and a series of environmental and climatic upheavals. Its geochronology remains, however, rather poorly constrained and highly debated. Here, we present high-precision zircon U-Pb dates for four ash beds from the Induan in South China, along with carbon isotope data. We use coupled Bayesian eruption age and Bayesian age-depth models to estimate the ages of our ash beds and to reinterpret the published ages of 25 ash beds from other four sections in South China, as well as to construct new age-depth models for each section. Our new age-depth models, integrated with biostratigraphic data, yield new age estimates for the following boundaries: Permian-Triassic (~251.867 Ma), Griesbachian-Dienerian (~251.562 Ma), Induan-Olenekian (~250.626 Ma), Smithian-Spathian (~249.236 Ma), and Olenekian-Anisian (~246.979 Ma). Calibration of the Early Triassic carbon isotopic record using the new age model reveals highly variable rates of individual carbon isotope excursions. For instance, the negative excursion across the Permian-Triassic boundary exhibits a rate of approximately -11.7‰ per 100 kyr-nearly seven times faster than the early Smithian negative excursion rate of approximately -1.7‰ per 100 kyr. This refined age model also provides a robust temporal framework to evaluate the tempo of biotic evolution in the aftermath of the Permian-Triassic mass extinction.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".