Moving towards a better understanding of iterative evolution: an example from the late Silurian Monograptidae (Graptolithina) of the Baltic Basin
Bibliographic record
Abstract
Abstract Iterative evolution has proved a difficult evolutionary phenomenon to study and interpret. Inferences of causality vary from study to study and quantitatively based phylogenetic reconstruction has never been attempted. In an effort to better understand iterative evolution we employed stratocladistics, gap analysis and disparity analysis to study the case of the Monograptidae in the aftermath of the late SilurianCyrtograptus lundgreniextinction event. Our combination of gap analytical and stratocladistic techniques allowed us to elucidate the evolutionary relationships between the studied taxa. Based on our stratocladistic results we recommend the generic reassignment of five monograptid taxa. The stratocladistic results, in conjunction with morphological disparity analysis suggest the presence of a persistent developmental potential for the emergence of iteratively evolving characters. This persistent potential appears to be limited by extrinsic ecological constraints, which would have relaxed in the aftermath of theC. lundgreniextinction event. Our findings indicate that iterative evolution in the late Silurian Monograptidae is a product of the interaction of both intrinsic and extrinsic constraints on the acquisition of the iteratively evolving character, with the exact causality being dependent on the particular character.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.000 |
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".