<title>Truncated Dempster-Shafer optimization and benchmarking</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Dempster-Shafer (DS) evidential scheme is notoriously CPU-intensive and requires a truncation mechanism for real- time operation within a realistic Multi-Sensor Data Fusion (MSDF) system. A truncation scheme consisting of at least 4 parameters has previously been proposed and shown to work well in a limited set of naval and airborne scenarios. The present study considerably expands the realism of the generated airborne scenarios (by using a simulator with ground truth), expands the related platform and emitter databases, benchmarks the CPU loading, optimizes the values of the parameters by requiring faster convergence to a single correct platform identification, and computes relevant Measures of Performance. It also compares the truncated DS scheme's method of ordering the propositions for the MSDF operator to other schemes such as possibility theory, plausibility decision rules, and the Expected Utility Interval approach. Most parameters are found to vary the database size and independence of sensor reports. In particular the need to keep more propositions than previously reported is quantified and schemes to dynamically adjust this number are proposed. The relevant thresholds also have to be simultaneously decreased as the database size increases. Furthermore the minimum amount of ignorance has to be kept at an appropriate level to recover from countermeasures included in some scenarios, or from badly trained ship classifiers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it