The effects of taphonomic color alteration upon skeletal recovery rates during surface searches
Bibliographic record
Abstract
Previous research shows that even expert-level recovery teams can miss osteological evidence. The present research examined recovery rates and distances using dispersed nonhuman (pig [Sus scrofa], white-tailed deer [Odocoileus virginianus], and mule deer [O. hemionus]) bones with different taphonomic color alteration. Searching was done by a Special Emergency Response Team of the Massachusetts State Police during researcher-guided walk-throughs in a simulated outdoor surface scene. The authors hypothesized that sun-bleached bones would be recognized from the greatest distances due to their starker contrast from natural leaf litter as opposed to control bones or those with soil/decomposition staining. The control sample was unaltered, dry bone without significant staining, and the stained bones had variable brown color from decomposition and/or shallow burial. The sun-bleached bones were spotted at an average distance of 8.76 m ± 7.75 m, while soil/decomposition-stained bones were spotted at 4.09 m ± 2.79 m and the control sample at 6.73 m ± 5.40 m. As determined by a two-way ANOVA test, the interaction between bone color and distance was significant (p < 0.001). Sun-bleached bones had a 100.0% recovery rate (60/60), control bones 96.7% (58/60), and soil/decomposition-stained bones 70.0% (42/60). In addition, bone type also had a statistically significant effect on distance (p < 0.001), and therefore the likelihood of being recovered. Even expert-level recovery teams can miss osteological evidence during surface searches, with natural bone camouflage factoring into recovery success rates. As a result, increased training and education surrounding taphonomically altered bones is necessary for all personnel involved in forensic surface searching.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".