Representing and Publishing Cyber Forensic Data and its Provenance Metadata: From Open to Closed Consumption
Bibliographic record
Abstract
Abstract—Role players of any forensic investigation process record chronologically all forensic data resulted from their investigation, in order to be presented to the juries in the court of law. When such results are recorded and posted, they are called chain of custodies (CoCs). The forensic data provided within these documents play a vital role in the process of forensic investigation, because they answer questions about how evidences are collected, transported, analyzed, and preserved since their seizure through their production in court. Provenance metadata accompany these forensic data to answer questions about the origin of these data and build trustworthy between role players and juries in order to make the tangible CoCs admissible in the court of law. Nowadays, with the advent of the digital age, the forensic investigation is not only applied to physical crime, but also on digital evidences. The
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".