High-resolution (centimetre-scale) GPS/GIS-based 3D mapping and spatial analysis of in situ fossils in two horned-dinosaur bonebeds in the Dinosaur Park Formation (Upper Cretaceous) at Dinosaur Provincial Park, Alberta, Canada
Bibliographic record
Abstract
Mapping of fossil sites represents an important aspect of palaeontology, because the data collected are required for interpreting the taphonomic and depositional history of the site, as well as the palaeoecology and behavior of the organisms. Methods for mapping and documenting certain vertebrate fossil sites, such as trackways, have drastically changed in recent years, with the integrated technologies of photogrammetry, laser scanning, and geographic information systems becoming standard practice, and providing digital, three-dimensional, and georeferenced data for analyses. Contrasting this technological revolution, the methods for mapping vertebrate bone accumulations, such as bonebeds, have changed little in recent decades, and are largely limited to two dimensions, are non-georeferenced, and produce static maps. Here, we present a novel test case in the mapping of two ceratopsid (Dinosauria: Ornithischia) monodominant bonebeds (mass death assemblages) that are documented digitally, fully georeferenced, and in three dimensions, using a combination of high-resolution (at centimetre-scale) global positioning system, photogrammetry, and geographic information systems. Importantly, accompanying spatial data (i.e., size and orientation) are collected in the field in the traditional manner and directly compared with values calculated from the digital map. Parameters describing bone length and orientation exported from the digital map are largely reflective of measured field data, with both size and orientation distributions being statistically indistinguishable, but with disproportionate error for elements smaller than 10 cm. Protocols and methods tested here will hopefully add to the discussion about the future of fossil bonebed mapping, specifically incorporating digital, three-dimensional, and fully georeferenced data into a powerful analytical tool.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".