Metamodeling-Based Drone Forensics Investigation: A Systematic Literature Review
Bibliographic record
Abstract
The drone forensics field has received a great attention in recent years due to the important role it plays in the investigation of incidents as well as the identification and tracking of attacking entities and actions.In an era where technology intersects with nearly every aspect of human life, drone forensics has emerged as a transformative force in medicine, biomedical research, and healthcare.The present paper reviews the literature of drone forensics to improve the body of knowledge and identify the underlying challenges relevant to the studies conducted in this field.In addition, the paper discusses how to define and integrate models from many domains of drone forensics using the metamodeling technique.This technique is applicable to various fields, particularly for standardizing purposes.Moreover, the present study involves the systematic literature review (SLR) which is provided in a section alongside the research topics; it serves as the main source of inspiration in the current work.As the literature does not comprise any study focusing on this issue with the use of SLR, this paper can contribute to filling this gap effectively.The SLR was carried out in the current study by the categorization of the existing literature parametrically using bibliometric analysis.The findings of this paper showed that the use of metamodeling in the drone forensics can make this field more homogeneous and less complex.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.041 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".