The Influence of Wounds, Severe Trauma, and Clothing, on Carcass Decomposition and Arthropod Succession in South Africa
Bibliographic record
Abstract
This study investigated the influence of different types of physical wounds as well as clothing on carcass decomposition and arthropod succession during all seasons over one year in South Africa. The trials for each season included six pigs, Sus scrofa, carcasses: two carcasses with wounds, one carcass was clothed the other was not; two carcasses with stab wounds, one carcass was clothed and the other was not; two carcasses with severe trauma wounds, one carcass was clothed and the other was not. The decomposition process and arthropod succession were not influenced by the absence or presence of wounds. Adult female Diptera did not select the wounds as oviposition sites. The presence of clothing caused a slight change in decomposition process only during summer and winter trials. The dominant Diptera in autumn and summer were Chrysomya marginalis and Chrysomya albiceps. In spring, it was Chrysomya chloropyga and C. albiceps and in winter it was Sarcophaga spp., C. chloropyga, Calliphora vicina, and Lucilia spp. During the warmer seasons, maggot predation by C. albiceps on C. marginalis was observed. In all seasons the Coleoptera were dominated by Dermestes maculatus and Necrobia rufipes, however, in summer, Thanatophilus micans and Histeridae spp. were also recorded.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".