Quantifying Similarity in Paleontological Settings: Accuracy, Complexity, and Macroevolutionary Implications
Bibliographic record
Abstract
In paleontological settings, similarity measures serve multiple roles, from arranging taxa into phylogenetic trees, to quantifying overlap between communities in terms of several interrelated factors. Any measure that attempts to incorporate such a wide variety of concepts will struggle to handle lost information, and the well-known biases of the fossil record represent a significant hurdle to accurate quantification of similarity. In this thesis I explore the accuracy of several well-known similarity measures when they are applied in paleontological settings, and discuss how measuring similarity can allow us to better understand long-term evolutionary dynamics. Using a combination of numerical models that simulate the biases of the fossil record, alongside field-based samples from rapidly lithifying environments, I demonstrate that only a small subset of popular similarity measures work in paleontological settings. I explore how the most accurate measures can be modified to incorporate additional information about inter-community overlap, expanding our conception of what constitutes a “similar” ecosystem. While articulating the assumptions underlying the most commonly used similarity indices, I wrote a proof demonstrating that one of the most popular similarity measures in paleontological research (convex hull overlap) does not work like other measures and can return inaccurate results even in unbiased settings. Finally, I looked at two key questions in the application of similarity to paleontological problems. The first of these issues involves the problem of a tie when classifying taxa into groups based on functional similarity. In classical classification protocols, when a given taxon is equally likely to belong to two or more groups, there is no clear solution. Here I explore how the mathematical approach of fuzzy set theory can offer a potential means of solving this problem. Finally, I explore how quantifying similarity can offer a possible mechanistic explanation for one of the most unusual patterns in the fossil record – the decoupling of the taxonomic and ecological consequences of a mass extinction event. My research provides the first quantitative evidence that functional similarity, long proposed to have a stabilizing effect on small-scale ecosystems, could potentially prevent widespread collapse in the face of a global climatic shift.
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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 | 0.048 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".