Diatom analysis: Reviewing the strengths, weaknesses, and impacts of modern research
Bibliographic record
Abstract
The purpose of this paper is to review the science underpinning diatom analysis and its ability to help determine death by drowning in a forensic context. This article evaluates the strengths and weaknesses of diatom analysis and looks at recent research to discern whether or not the scientific techniques still have value today. Although weaknesses exist (diatoms can be introduced into bodies through a variety of ways before death, passively enter tissues during the decomposition process, may not be found in some cases of drowning, and has issues regarding false-positive tests and sensitivity when environmental concentrations are low), it will be seen that modern research has addressed many concerns and that accuracy of analysis is continuing to improve. It will be shown that the strengths (environmental specificity, seasonal variability, significant quantitative differences between drowned and non-drowned victims) combined with the fact that diatoms being found in bone marrow is one of the only answers to get a definitive diagnosis of death by drowning, that diatom analysis still has an important role to play in forensics today.
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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".