Detection of structural degradation of porcine bone in different marine environments with Raman spectroscopy combined with chemometrics
Bibliographic record
Abstract
Abstract The investigation of time since exposure and taphonomic alteration of bone in marine environments is crucial in forensic sciences. In this study, we explored the diagenetic changes to juvenile porcine bone in two different environmental marine contexts (submerged and intertidal) and how seasonal variation at time of deposition impacted on the pattern of taphonomic alteration in bone during early stages of exposure, from 6 to 24 weeks. The analysis was conducted using Raman spectroscopy combined with chemometrics. Principal component analysis of the Raman data of the recovered bones (either summer or winter season in 2014) showed that the main chemical changes occurred in the bioapatite (phosphate band, ν1(PO43−) at 961 cm−1) and organic constituents in the bones and depended on the exposure environment. Support vector machine (SVM) classification analysis classified the samples based on bone type, exposed environment and season, with high accuracy (>80%), but exposure time was less accurate (56%), although still higher than chance. When applying a SVM regression, time since exposure could be predicted with a ±5‐week uncertainty. This study illustrated the potential and limitations of using Raman spectroscopy to detect structural degradation of bone in different marine environments for forensic purposes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".