Fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories
Bibliographic record
Abstract
We address the problem of fusing ESM reports by two evidential reasoning schemes, namely Dempster-Shafer theory and Dezert-Smarandache theory. These schemes provide results in different frames of discernment, but are able to fuse realistic ESM data. We discuss their advantages and disadvantages under varying conditions of sensor data certainty and fusion reliability, the latter coming from errors in the association process. A thresholded version of Dempster-Shafer theory is fine-tuned for performance across a wide range of values for certainty and reliability. The results are presented first for typical scenarios, and secondly for Monte-Carlo studies of scenarios under varying sensor certainty and fusion reliability. The results exhibit complex non-linear functions, but for which clear trends can nevertheless be extracted. A compromise has to be achieved between stability under occasional miss-associations, and reaction time latency under a real change of allegiance. The alternative way of reporting results through Dezert-Smarandache theory is studied under similar conditions, and shown to provide good results, which are however more dependent on the unreliability, and slightly less stable. In this case however, the frame of discernment is larger, and permits additional interpretations, which are outside the scope of Dempster-Shafer.
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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 | 0.028 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".