A review of forensic applications of physicochemical parameters of soil beneath decomposed cadavers
Bibliographic record
Abstract
Soil and its many attributes are often used to assist the police or environmental investigators involved in crime scene investigation, exhumation, and even linking a suspect to a crime scene. Soil consists of organic and inorganic compounds, water, and air at different compositions and concentrations. These unique soil features may serve as useful evidence in forensic investigations. Soil parameters can individually or cumulatively act as identification markers or aid in supporting other evidence in criminal investigations. The soil might link an individual to a crime scene as soil can be transferred to the suspect’s footwear, tires, or clothes. This evidence indirectly correlates to the presence or absence of the person at the crime scene. This review article highlights the application of various soil physicochemical parameters in solving forensic cases. Furthermore, we have summarised the results of 74 research articles related to forensic soil chemistry published from the year 1985 to 2021. In this review manuscript, a literature search was performed in Medline, CINAHL, Scopus, and google search with thorough assessment based on their content and significance. This article aims to review papers on the physicochemical parameters of soil which include soil pH, electrical conductivity, moisture content, soil extractable phosphorus, total carbon, total nitrogen, soil extractable ammonium and nitrate, soil colour and soil texture beneath carcasses placed on soil surface as well as buried in graves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".