Stoch-aptation: a new term in evolutionary biology and paleontology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it