Testing Different Search Methods for Recovering Scattered and Scavenged Remains
Bibliographic record
Abstract
Vertebrate scavengers are primary mechanisms for scatter and disarticulation of human remains in rural habitats. Because recovery of the body can be hampered by the degree of scatter due to scavengers, the methods used to search for body parts will influence how much is found and the length of time taken to recover the body. We compared the frequency of scavenging by vertebrates in two different habitats, a deciduous forest and a tall grass meadow, and measured the time taken to search for scattered remains within a designated search area using four methods. Freshly killed 23 kg pigs were placed in either a forest or tall grass meadow habitat, and scavenging by vertebrates was observed over a 5–6 day period. Subsequently, the link, line, zone, and spiral methods were used to search for remains within a 21 m2 search area. Three of 5 pigs in the forest and 4 of 5 pigs in the meadow habitat were scavenged by a variety of vertebrates. Mean time to search the designated area around each pig differed between the forest and the meadow, but not by search method. Mobility within each habitat likely explains the difference in search times, and also accounts for some of the variability between search methods.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".