Injuries in deep time: interpreting competitive behaviours in extinct reptiles <i>via</i> palaeopathology
Bibliographic record
Abstract
For over a century, palaeopathology has been used as a tool for understanding evolution, disease in past communities and populations, and to interpret behaviour of extinct taxa. Physical traumas in particular have frequently been the justification for interpretations about aggressive and even competitive behaviours in extinct taxa. However, the standards used in these interpretations have been inconsistent and occasionally questionable, and knowledge of extant reptile pathology is limited. Interpretations about the timelines and causes of unhealed bone damage are unreliable, and so behavioural implications from these marks are dubious. Even in the case of tooth marks, perimortem damage can be difficult to distinguish from postmortem alteration. In this review, methods from the anthropological sciences are adapted for the purposes of palaeontology, especially in establishing a new framework to distinguish antemortem traumatic damage from other similarly presenting features like sediment encrustation, postmortem damage/taphonomic features, variants of anatomical features, and non-traumatic palaeopathologies. Even in cases where traumatic palaeopathologies are accurately macroscopically identified, noting isolated incidences may not provide sufficient evidence to interpret behaviour at any taxonomic level. Future research directions in modern reptile pathology are proposed to improve the efficacy of traumatic palaeopathologies as a tool in interpreting extinct reptile behaviours.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".