Calibrating Early Triassic biotic rebound and carbon cycles based on coupled Bayesian zircon eruption age and Bayesian age-depth models
Bibliographic record
Abstract
Robust time scale is essential for understanding geological processes and biotic evolution. Following the Permian-Triassic mass extinctions, the Early Triassic recorded a series of climatic, environmental and biotic events. However, the tempo of these events remains poorly constrained. In this work, we present four new high-precision zircon U-Pb dates from the Induan in South China using high precision CA-ID-TIMS techniques. We then adopted coupled Bayesian eruption age and Bayesian age-depth models to interpret these dates and built age model, based on which, we provided, for the first time, absolute time estimations for the Griesbachian-Dienerian and Induan-Olenekian boundaries, i.e. 251.562 +0.090/-0.101 Ma and 250.626 +0.140/-0.214 Ma, respectively. Then, we applied the same analytical approach on published U-Pb dates of 25 ash beds. The resulting age-depth models, combined with biostratigraphic data, allow us to update age estimations for all Early Triassic stage/substage boundaries. The new time scale shows significant differences from those based on cyclostratigraphy, as well as some minor differences from the low MSWD weight mean age interpretations. The timing of three negative and four positive excursions of Early Triassic carbon isotope were precisely constrained based on the new age models. The new age models also provide a robust timeline to evaluate biotic evolution in the aftermath of the Permian-Triassic mass extinction. For instance, the first remarkable biotic rebound in the late Griesbachian is ~0.3 Myr after the Permian-Triassic mass extinction, much faster than expectation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".