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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".