Simulated weathering of dinosaur tracks and the implications for their characterization
Bibliographic record
Abstract
Digital models of the tracks of bipedal theropod and ornithopod dinosaurs, and the quadrupedal tracks of a sauropod, were computationally eroded to investigate the effects of erosion on the shapes, sizes, and diagnostic details of fossil tracks. Narrow and (or) angular details, such as claw marks, interdigital ridges, and internal ridges, are removed early in erosion, creating the potential for misidentification of eroded theropod tracks as those of ornithopods. However, with the erosion models presented here, all tracks retain their basic shapes as indicated by the relative constancy of their interdigital angles and by the relative constancy of their footlength:footwidth ratios. Surface lengths of tracks did not increase significantly with erosion, so that dinosaurian hip height and speed estimates derived from trackways would not be greatly in error if based on eroded surface tracks. Synthetic undertracks from the surface tracks were also produced using information from published physical models of track formation. The differences between a weathered surface track and a freshly exposed, simulated undertrack are sufficient so that the two model tracks would not be confused. Large, rounded tracks are much better at retaining their characteristics than small, angular tracks, with the implication that large tracks may be over-represented in the fossil record, but they may be more reliably attributed to the appropriate trackmaker. This would bias estimates of dinosaur taxonomic diversity and body size ranges based on trackway evidence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".