Statistical methods for paleodemography on fossil assemblages having small numbers of specimens: an investigation of dinosaur survival rates
Bibliographic record
Abstract
We describe statistical methods to formulate and validate statements about survival rates given a small number of individuals. These methods allow one to estimate the age-specific survival rate and assess its uncertainty, to assess whether the survival rates during some age range differ from the survival rates during another age range, and to assess whether the survivorship curve has a particular shape. We illustrate these methods by applying them to a sample of 22 Albertosaurus sarcophagus individuals. We show that this sample is too small to provide any confidence in the claim that this species had a “convex” survivorship curve arising from age-specific survival rates that decreased monotonically with age. However, we show that a sample of 50 to 100 individuals has reasonable statistical power to support such a claim. There is evidence for the much weaker claim that average survival rates for ages 2 to 15 were higher than survival rates for later ages. Finally, we describe one way to account for size-dependent fossilization rates and show that a plausible positively-size-dependent fossilization rate results in a substantially non-convex survivorship curve for A. sarcophagus .
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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 | 0.103 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".