Stoch-aptation: a new term in evolutionary biology and paleontology
Bibliographic record
Abstract
Following two seminal papers published in Paleobiology by Stephen Jay Gould and Elisabeth Vrba several decades ago, I suggest a new term (stoch-aptation) to refer to those individual traits or sets of traits that provide, just by chance, fitness advantages to species when faced with catastrophes (i.e. geological events triggering massive mortality), and that may lead to the origin of taxonomical entities above the species level. I provide as an example of stoch-aptations the set of features that helped mammals pass the Cretaceous-Paleogene transition, as well as traits behind the success of living fossils. However, the identification of specific stoch-aptations can be difficult. This missing term is necessary and useful to (a) consolidate the idea of selection at different hierarchical levels, (b) acknowledge the role of chance in the evolution of higher taxonomical categories, and (c) think of the role of geological catastrophes as generators of innovation.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".