Analysis of Class Characteristics of Reciprocating Saws<sup>,</sup>
Bibliographic record
Abstract
Criminal dismemberment is accomplished using a variety of tools and frequently used to dispose or facilitate the transport of human remains in an attempt to hinder forensic investigation. The present research examined features that may differentiate cuts made in bone by various commercially available reciprocating saw blades. The partial limbs of adult white-tailed deer (Odocoileus virginianus) were used as a proxy for human remains and were cut using five reciprocating saw blades and a hand-powered hacksaw. The resulting false start and complete kerfs were examined macroscopically and microscopically. Kerf characteristics in which significant differences (p ≤ 0.05) between reciprocating blades were noted including minimum kerf width, kerf false start shape, presence of cut surface drift and harmonics, exit chipping size, and striation regularity. Interblade differences generally reflect class characteristics previously established for hand-powered blades. The present research may aid in the identification of reciprocating saw use in forensic contexts.
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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 | 0.000 |
| 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 | 0.043 |
| 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".