Combination rules of evidence for situation assessment and target identification
Bibliographic record
Abstract
In this paper, the combination rules, such as the Dempster-Shafer's (D-S) combination rule, the Yager's combination rule, the Dubois and Prade's (D-P) combination rule, the DSm's combination rule and the disjunctive combination rule, are applied to the situation assessment and target identification problems. Given two independent sources of information with different resolutions, the results from each combination rule of evidence are analyzed. It is observed from these results that the DSm's rule is the fastest in arriving at a decision compared to the other three rules, while the disjunctive combination rule is the slowest. The Yager's rule yields the same identification results for the situation assessment as the Dubois and Prade's rule. Moreover, the decision-making of the D-S' rule is faster than that of the Yager's as well as of the Dubois and Prade's rules, however, slower than that of the DSm's rule
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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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".