Correlation between saw blade width and kerf width
Bibliographic record
Abstract
Most studies of saw marks have focused on morphological characteristics and their utility in identifying saws suspected to have been utilized in cases of criminal dismemberment. The present study examined the extent to which metric analysis may be used to correlate saw blade measurements with minimum kerf widths (MKWs). A sample of 56 partially defleshed white-tailed deer (Odocoileus virginianus) long bones was utilized as proxy for human remains. The long bones were cut using a variety of commercially available saws, including 11 manual-powered and 5 mechanical-powered saws. A total of 496 false start kerfs (FSKs) were created. Two experiments were performed, with the first test examining the MKWs of FSKs produced on specimens that were restrained using a bench vise, while the second test analyzed the MKWs of FSKs produced on minimally restrained specimens. Statistical analysis using Hierarchical Linear Modeling (HLM) indicated a positive relationship between saw blade width (mm) and MKW, with blade width (p < 0.001) and the overall difference between the mechanical- and manual-powered saws (p = 0.029) tested, reaching statistical significance. A comparison of MKWs produced using manual-powered saws on restrained and minimally restrained bones suggests that restraint condition (p = 0.009) has a statistically significant effect. In comparisons of MKWs to blade widths, the average ratio for mechanical-powered saws is 18.7% greater than the average ratio for manual-powered saws. While the mode of the ratios was 1.42, thus supporting the general rule that MKW does not exceed 1.5 times blade width, multiple individual ratios did surpass 1.5.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.021 |
| 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".