Evolution and global correlation for strontium isotopic composition of marine Triassic from Huaying Mountains,eastern Sichuan,China
Bibliographic record
Abstract
Strontium isotope stratigraphy (SIS) has progressively become an efficient chemostratigraphic tool in the research and correlation of global geological events, such as global sea level fluctuations, orogeny, and paleoclimatic and paleoenvironmental changes. In this paper, 87Sr/86Sr ratios of the Triassic marine carbonate rock samples from Huaying Mountains, eastern Sichuan are measured, and the corre-sponding Sr-isotopic curve is constructed, based on the fundamental principle of strontium isotope stratigraphy, analysis of rock fabric, luminescent intensity, chemical composition and representative evaluation for the coeval seawater information. The 87Sr/86Sr ratios show a rapid rise from 0.70721 near the Permian-Triassic transition to 0.70830 at the end of Early Triassic, and then they decline rapidly to 0.70787 in the early Middle Triassic. These data are generally coincident with 87Sr/86Sr ratios of coeval seawater from previous papers, and the curve is also similar to other previous curves. This indicates that the global geological events are the most important controlling factors to the strontium isotope evolution of the global seawater in the Early and Middle Triassic.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".