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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".