The occurrence and genesis of transfer traces in forensic science: a structured knowledge database
Bibliographic record
Abstract
While forensic science is generally focused on associating a trace to its source, trace’s relevance is best addressed at the activity responsible for its genesis. Recurring studies show the potential of the Bayesian approach in order to address activity level’s propositions in a rational and transparent manner. The objective of this research is to identify and review literature and models for transfer traces to create a relevant database for activity level interpretation. As of December 17<sup>th</sup>, 2020, a thorough review of 2042 existing peer-reviewed publications and studies concerning transfer traces has been conducted. The data have been classified by different criteria such as, the type of trace, year of publication, and type of study (i.e. population). Every publication has been critically analyzed according to its relevance, among others, with regards to a Canadian environment. This process identified research that needed to be completed. A database collecting publication and data on activity level assessment has been created. This database is available for consultation to laboratories, police agencies, lawyers and universities, thus contributing to the transparency of the expert opinion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".