Molecular identification of fungi associated with advanced decomposition at a human taphonomy facility in Canada
Bibliographic record
Abstract
Forensic taphonomy investigates the postmortem processes of human remains, focusing on the environmental factors that influence decomposition. Recent studies have highlighted the potential forensic relevance of fungi in this context, but the knowledge base remains limited. This study explored fungal communities associated with outdoor human decomposition at the REST[ES] facility in Quebec. Nested PCR amplification and Illumina MiSeq sequencing were used to identify fungal species on discolored patches of twelve samples of desiccated soft tissues from three donors. Twelve fungal species were putatively identified, some of which were previously unknown on human remains, including Leucosporidium yakuticum , Tausania pullulans , and Fusicolla species. These fungi may contribute to tissue discoloration and following longitudinal investigation, could serve as biomarkers for forensic reconstructions, including place and time of death. This study emphasizes the need for further research into the role of fungi in human decomposition processes and their applications in forensic science. • We identified the first fungal species associated with human decomposition in Quebec. • Twelve fungal species were identified, some previously unknown on human remains. • The molecular identification of fungi avoided bias toward only culturable species.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".