Size-driven preservational and macroecological biases in the latest Maastrichtian terrestrial vertebrate assemblages of North America
Bibliographic record
Abstract
Abstract The end-Cretaceous (K/Pg) mass extinction event is the most recent and well-understood of the “big five” and triggered establishment of modern terrestrial ecosystem structure. Despite the depth of research into this event, our knowledge of upper Maastrichtian terrestrial deposits globally relies primarily on assemblage-level data limited to a few well-sampled formations in North America, the Hell Creek and Lance Formations. These assemblages disproportionally affect our interpretations of this important interval. Multiple investigations have quantified diversity patterns within these assemblages, but the potential effect of formation-level size-dependent taphonomic biases and their implications on extinction dynamics remains unexplored. Here, the relationship between taphonomy and body size of the Hell Creek Formation and Lance Formation dinosaurs and mammals are quantitatively analyzed. Small-bodied dinosaur taxa (<70 kg) are consistently less complete, unlikely to be articulated, and delayed in their description relative to their large-bodied counterparts. Family-level abundance (particularly skeletons) is strongly tied to body mass, and the relative abundance of juveniles of large-bodied taxa similarly is underrepresented. Mammals show similar but nonsignificant trends. The results are remarkably similar to those from the Campanian-aged Dinosaur Park Formation, suggesting a widespread strong taphonomic bias against the preservation of small taxa, which will result in their seemingly depauperate diversity within the assemblage. This taphonomically skewed view of diversity and abundance of small-bodied taxa amid our best late Maastrichtian samples has significant implications for understanding speciation and extinction dynamics (e.g., size-dependent extinction selectivity) across the K/Pg boundary.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".