Automated forensic soil mineral analysis; testing the potential of lithotyping
Bibliographic record
Abstract
Abstract In the investigation of serious crimes, soil can be, in some cases, a very valuable class of trace evidence. The complexity of soil is part of the reason why it is useful as trace evidence but is also an inherent problem, as there are many different parameters in a soil sample that could potentially be characterized. The inorganic components of soils are dominated by minerals, along with anthropogenic particulate grains; thus, the analysis of soil mineralogy as the main technique for inorganic forensic soil characterization is recommended. Typical methods that allow the bulk mineralogy to be determined, such as X-ray diffraction (XRD), do not allow the texture of the particles to be characterized. However, automated scanning electron microscopy (SEM) provides both modal mineralogy and also allows particle textures to be characterized. A recent advance in this technique has been the ability to report the modal mineralogy of a sample as ‘lithotypes’, which are defined on the basis of a combination of mineralogy and other parameters, such as grain size and mineral associations. Defined lithotype groups may include monominerallic grains but also, importantly, allow the automated quantification of rock types and other anthropogenic materials. Based on a simulated forensic scenario, the use of lithotyping is evaluated as an aid in the analysis of soil samples. This technique provides additional discrimination when comparing different soil samples.
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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".