Lopingian (Late Permian) high‐resolution conodont biostratigraphy in Iran with comparison to South China zonation
Bibliographic record
Abstract
Abstract Lopingian (Late Permian) conodonts and stratigraphy in northwest and central Iran have become hotly debated issues recently. We here use a sample‐population approach, to develop a high‐resolution conodont biostratigraphic framework for the Lopingian of Iran based on a re‐examination of collections studied by Sweet from the Kuh‐e‐Ali Bashi area, northwest Iran; samples from the Abadeh C section and a nearby Permian‐Triassic boundary section in the Abadeh area; and on published data. Six Wuchiapingian conodont zones, theClarkina dukouensis, C. asymmetrica, C. leveni, C. guangyuanensis, C. transcaucasicaandC. orientaliszones, and eight Changhsingian conodont zones, theClarkina wangi, C. subcarinata, C. changxingensis, C. bachmanni, C. nodosa, C. yini, C. abadehensisandC. hauschkeizones, are described and figured. Diagnoses of ontogenetic characteristics to population variations of all the zone‐naming species are re‐described based on a sample‐population taxonomic concept. The high‐resolution Lopingian conodont zonation in Iran is closely correlative with its counterpart in South China. However, slightly different evolutionary trends inClarkinapopulations existed at the very end of the Changhsingian in Iran and South China. This reflects a geographical cline and/or facies dependence and endemism inClarkinapopulations rather than stratigraphic incompleteness of sections in either Iran or South China. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".