Computational Topology Techniques Help to Solve a Long-Lasting Forensic Dilemma: Aldo Moro’s Death
Bibliographic record
Abstract
Here we show how a recently-introduced method from algebraic topology, namely proximal planar vortex 1-cycles, might be helpful in detecting hidden features of the shapes and holes in images, therefore contributing to the solution of both cold and fresh forensic cases. In particular, we test the efficacy of this technique by assessing one of the most puzzling cases of recent history, i.e., Aldo Moro’s death. Terrorists of the Red Brigades claimed that they killed Moro when he was placed inside the trunk of a car,shooting him with a barrage of bullets. We demonstrate, based on the analysis of the photographs taken during the autoptic procedure, that the terrorist’s account does not hold true. Our results, showing different series of shots, point towards a three-step execution, with the first phasestaking place outside the car. In conclusion, the novel forensic analysis method introduced in this paper permits the evaluation of a collection of vortex cycles/nerves equipped with a connectedness proximity, which makes it possible to assess unexpected spatial clusters in photographs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".