The biotic effects of large bolide impacts: size versus time and place
Bibliographic record
Abstract
Abstract In estimating the biotic effects of large terrestrial impacts we are reliant upon apparent crater diameter as a proxy for impact magnitude. This underlies the ‘kill-curve’ approach which graphs crater diameter directly against likely percentage losses of taxa. However, crater diameter is a complex product of syn- and post-impact processes that can be site-dependent. Furthermore, location (global positioning) and timing (moment in geological history) also strongly influence biotic effects. We examine four of our largest and best-documented Phanerozoic impacts to explore this more holistic size–time–place relationship. Only the c. 180 km end-Cretaceous Chicxulub crater (Mexico) links to any substantial immediate extinction and some of the worst effects stem from where it struck the planet (a continental margin carbonate platform site) and when (a time of high regional and global biodiversity). Both the c. 100 km late Triassic Manicouagan crater in NE Canada (arid continental interior, low regional and world biodiversity) and the c. 35 Ma 100 km Popigai crater, Siberia (continental arctic desert) provide much less damaging scenarios. However the c. 90 km Chesapeake Bay crater, Eastern USA (also c. 35 Ma) marks a far more sensitive (Chicxulub-like) site but it also proved relatively benign. Here the rheologically varied shallow marine target site produced an anomalously broad crater, and the scale of the impact has evidently been overestimated. We offer a new approach to the graphical prediction of biotic risk in which both crater diameter and a generalised time/place factor we term ‘vulnerability’ are variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".