DNA profiles from flip-open cell phones
Bibliographic record
Abstract
Flip-open style cell phones were investigated for the potential to produce quality genetic profiles that could be used in forensic casework. Swabs were taken of the outside/back and the inside ear speaker of ten flip-phones on two occasions – prior to and seven days after cleaning with 95% ethanol. Buccal swabs were collected as exemplars. The samples were amplified using the AmpFlSTR ProfilerPlus PCR Kit for 35 cycles and STR profiles were generated using an ABI Prism 310 Genetic Analyzer and GeneMapper ID analysis software v3.2. The phone profiles were compared to the references and to each other, to assess the quality of the profiles. The completeness of the profiles varied greatly, even within an experimental condition. There was no significant difference in the percentage correct alleles or in the number of drop-in alleles in the DNA profiles for the outside/inside locations or for the pre/post-cleaning times. The findings of this study demonstrate the need for collecting multiple samples from discrete locations on a cell phone if such evidence is encountered in forensic cases.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".