Two-Dimensional Evidence Reliability Amplification Process Model for Digital Forensics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Being related to law and state-of-the-art technology, digital forensics needs more discipline than traditional forensics. The variety of types of crimes, distribution of networks and complexity of information and communication technology, add to the complexity of the process of digital investigations. A rigorous and flexible process model is needed to overcome challenges and obstacles in this area. In this paper we propose a digital forensics process, called "two-dimensional evidence reliability amplification process model", which presents a detailed digital forensic process model in five main phases and different roles to perform it. At the same time, this iterative process addresses four essential tasks as the umbrella activities that are applicable across all phases and sub-phases. We have also developed a hypothetical solution based on intersection of events and exploit mathematical operations and symbols for making an algorithm to increase the reliability of evidence. This process model is detailed enough to describe the investigation process so that it could possibly provide a guideline that investigators can take advantage of it during a forensics investigation process.
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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 | 0.000 |
| 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it