Insect Faunal Succession and Development of Forensically Important Flies on Deer Carcasses in Southwest Virginia
Bibliographic record
Abstract
Forensic entomology has become synonymous with medico-legal entomology and involves the use of insects in legal and criminal investigations. Insects have been used as evidence in cases of wrongful death of humans and in wildlife poaching cases for many years. The first jail time sentence for wildlife poaching in Manitoba, Canada was awarded after insect evidence was used to create a timeline for the crime. In the interest of advancing the science of forensic entomology, insect faunal succession was studied on four white-tailed deer carcasses in southwest Virginia in the summers of 2009 and 2010. The patterns of insect succession between the summers of 2009 and 2010 were similar at ± = 0.05. Necrophagous insects arrived in a successional pattern as has been observed on other animal models (e.g. pigs) during past studies conducted in southwest Virginia. To further explore the role of wildlife specific variables to forensic entomology, larvae of Phormia regina, Meigen, were reared on pork and venison in a laboratory at Virginia Tech. Environmental rearing conditions were 30" C, 75% RH and 14:10 hour light dark cycle. Significant differences in lengths of 3rd instar and combined overall maggot lengths were found for maggots reared on the different meat sources. Mean adult weights and wing lengths of venison-reared flies were significantly greater than those reared on pork at ±=0.05.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".