Sequencing the graptoloid clade: building a global diversity curve from local range charts, regional composites and global time-lines
Bibliographic record
Abstract
SUMMARY Even range charts from the best graptoloid-bearing sections can be improved by adding information from nearby sections. Traditionally this is achieved on a scale of discrete biozones. Zonal composites improve upon the taxon richness of individual sections but lose resolving power; they artificially cluster range-end events at zone boundaries. Graphical and numerical methods allow composite sections to be constructed on continuous scales that match or exceed the resolving power of individual sections. Using a database of 582 graptoloid-bearing sections that together preserve 2214 distinct graptoloid taxa, we show that constrained optimization algorithms can construct objective, reproducible, global time-lines for the entire Ordovician to early Devonian graptoloid clade. The underlying database of locally observed ranges allows standardization for sampling unevenness. The quality and geographic origins of support for composite taxon ranges are explicitly revealed. A high precision, interval-free, standing species richness curve derived by this approach reveals the diversity trajectory of the graptoloid clade and of the families of which it is composed. Major reorganizations of the clade at the family level took place after diversity minima in the late Darriwilian and Hirnantian. Glacial events recognized on sedimentological evidence coincide with diversity minima.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".