Temperature estimations of heated bone: A questionnaire-based study of accuracy and precision of interpretation of bone colour by forensic and physical anthropologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The colour of thermally altered bone, recovered from archaeological and forensic contexts, is related to the temperature(s) to which it was exposed. As it is heated bone changes in colour from ivory white, to brown and black, to different shades of grey and chalky white. It should be possible to estimate exposure temperature based on visually observable changes in colour. In forensic casework the temperature that human remains have been subjected to can reveal information about the existence and nature of foul play. Therefore, it is important to understand the accuracy and precision of visual methods of temperature estimation. Twenty-eight forensic and/or physical anthropologists estimated the temperature that fourteen bone samples had been subjected to based only on their colour via an online questionnaire. Bone samples shown in the questionnaire ranged from unheated to having been heated at 1200°C. Respondents were given two options to base their estimates on, resulting in a multiple response analysis. The results suggest it is difficult to identify the correct temperature range based solely on colour. Most respondents felt confident enough to opt for a single option, which may have contributed to a relatively high number of incorrect estimates. Low accuracy and precision were found for most of the temperature ranges, especially in the lower and middle categories. This study demonstrates that caution should be taken in the reliance upon temperature estimates of thermally induced colour changes in bone and the need for further research and improved methods.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.021 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it