Quantification of bone surface textures: exploring a new method of ontogenetic ageing
Bibliographic record
Abstract
Abstract Identification of ontogenetic age classes plays an important role in the fields of zoology, palaeontology and archaeology, where accurate age classifications of (sub)fossil remains are a crucial component for the reconstruction of past life. Textural ageing—the identification of age-related bone surface textures—provides a size-independent method for age assessment of vertebrate material. However, most of the work so far is limited to qualitative results. While qualitative approaches provide helpful insights on textural ageing patterns, they are heavily subject to observer bias and fall short of quantitative data relevant for detailed statistical analyses and cross-comparisons. Here, we present a pilot study on the application of 3D surface digital microscopy to quantify bone surface textures on the long bones of the grey heron ( Ardea cinerea ) and the Canada goose ( Branta canadensis ) using internationally verified roughness parameters. Using a standardised measuring protocol, computed roughness values show a strong correlation with qualitative descriptions of textural patterns. Overall, higher roughness values correspond to increased numbers of grooves and pits and vice versa. Most of the roughness parameters allowed distinguishing between different ontogenetic classes and closely followed the typical sigmoidal animal growth curve. Our results show that bone texture quantification is a feasible approach to identifying ontogenetic age classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".