Uncertainty representations for a Vehicle-Borne IED surveillance problem
Bibliographic record
Abstract
The aim of this paper is to detail further a Vehicle-Borne IED scenario proposed as an uncertainty modeling challenge to the information fusion community by the Evaluation of Techniques for Uncertainty Representation (ETUR) working group. This enrichment of the basic scenario is partly based on the careful comparison of formalizations published thus far by four uncertainty modeling experts as well as on the authors expertise in surveillance system design and risk assessment. The main additions reside in the exploitation of the temporal and spatial dimensions of the IED scenario initial statement. The authors show that the compromise between the expected risk, the time to certainty and time to intervene is central to the modeling of this very basic scenario and should be exploited further. According to the analysis of the formalizations of the VBIED scenario already published is seems also of interest to introduce the notion of agents to clarify the definition of state spaces. From the proposed model elements the authors expect that the scenario can be extended for more practical uses by allowing the addition of historical datasets from which a priori knowledge can be extracted, measurements be made from maps, and available resources balanced against expected risk.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".