Issues and opportunities in urban forensic geology
Bibliographic record
Abstract
Abstract Geological trace evidence including, for example, soil, sand and rock dust has been examined using a wide range of analytical techniques. Whilst such materials are common in rural locations, in urban areas, such geological materials are often perceived to be restricted to parks, recreational areas, gardens and waste ground. However, both geological materials and the wide range of analytical methods used to characterize them are much more applicable to the whole urban environment than is generally realized, with the main differences being the types and amounts of sample analysed and the methods adopted. The range of geological applications can be summarized as those deployed at the broad (decimetres–kilometres) to small (millimetres–decimetres) scale. The broad spatial variation in soil, roadway, water, buildings materials, and wind- or water-borne particles can be contrasted with the variation in urban materials from dwellings to streets or gardens and parks, along with the micro-spatial and stratigraphical variation in each. In addition, geological principles and techniques that have not been used before can be applied to urban materials to provide comparisons of material that were not previously achievable, or to add a further proxy to established methods. The latter point is demonstrated with a case study using X-ray diffraction and QEMSCAN® of a criminal case where building plaster with peculiar qualities could be compared between a suspect's vehicle and plaster present along the escape route from a murder scene.
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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".